Next up will be given by Emil Mathieu, who is currently a postdoc in Cambridge. Take it away. Thanks, Mayelou, for the introduction, I guess the invite. Indeed, I'm a postdoc in Cambridge, and so I'll talk about geometric neural diffusion processes where we basically propose a probabilistic model for modeling tensor fields and feature fields. And I guess I'll give more motivation and details in what follows. So this is joint work with Vincent de Tortua, Michael Lechisson, Valentin de Bortilly, Iwaite, and Rich Dono, which I guess are all collaborators either in Cambridge or Oxford or in Paris. So first I'll have a very brief intro to joint modeling. I guess you try to motivate why do we care about this probabilistic setting. Then some background and continuous diffusion models, although I guess quite a lot of that material I guess has already been introduced in the previous talk. And then I'll show how we can extend that setting to the function space in order to model stochastic processes and eventually how to model feature fields, how to incorporate geometric constraints into the model. So first on Gibra to modeling, so there's quite some settings where we truly care about this kind of probabilistic modeling and not just a deterministic guess. So one is in molecular confirmation generation where say you have access to a molecular graph and you like to predict the 3D structure of that molecule. And so you could kind of just have your graph neural network that takes that as input into a 3D structure, but because of temperature fluctuations, this 3D structure is actually not fully frozen, it kind of fluctuates. And also depending on the specific choice of molecule, there might be like different kind of meta-stable structure in which this molecule kind of like could be. So it doesn't really make any sense to like kind of make one guess because actually there's like a full distribution over the 3D space of each of the atoms. So here's kind of like intrinsically random, so you'd like your model to model and incorporate that randomness. Another one would be for instance in meteorology, let's say you like to predict like precipitation in the following like hours or days or even weeks and that's actually like extremely tricky. I mean I guess I'm not a physician myself, but having told you like atmospheric physicians it seems like like the physics is kind of like still like not fully well understood and it's almost I think also like quite chaotic. So small like change in the initial condition would tell you that it's going to rain here but actually not rain like 500 meters away. And so here's more like the kind of like the lack of knowledge due to the lack of understanding of the physics but also of data that would make us like incorporate that like of good prediction of the essence into the modeling so that we kind of know how much we actually don't know. So instead of predicting like how much precipitation there'll be, we'll have like a full distribution over you know the precipitation. So I guess this like a different perfectly where it's kind of like there's some interesting randomness and the second example where it's more like we want to introduce uncertainty in the modeling to capture or kind of like you know limited knowledge about the problem. And so deep dive to models are basically like neural network kind of based model that parametrize a property distribution and so the idea is that you know we'd like to be able to either or both draw samples from the distribution or also you know evaluate the likelihood of some kind of samples under that model. And we kind of typically assume that we'll have access to samples from a digital distribution you know some kind of data set that we have access to and because in the setting where we have access some kind of like a normalized density or some kind of energy usually people refer that as like a sampling problem. And most of I guess like following the previous talk, most of probabilistic models we kind of follow into this kind of like quite a level framework where one would start with some kind of distribution. So for instance a normal or either something that's like a checkerboard and the idea is to have a parametric transmission to push forward that density along that transmission and that would induce another distribution that's like potentially like quite more complex. And then the idea is to like tune and learn the parameters of that transmission so that the induced probability distribution would kind of like fit you know would be like pretty close to a digital distribution that you'd like to fit like the data distribution that you care about. And once you've done that you can generate new samples or evaluate any you know the likelihood of your model for like if you give a new sample. So that's kind of the general framework. And diffusion models like do indeed fit into I guess the setting and as that remain previously they're like they've been shown to externally well in practice and so that's why we kind of in this talk build on this class of model. So I guess there's some of the lab is still like give a brief background on continuous diffusion models. So the key idea is really to start with some kind of like you know data samples so we call so I guess different slight difference with the previous talk is that I the time is kind of like in there. So we'd say like the initial data distribution you know the samples would come from like y is zero so t equals zero and then we like knows them until we get to like some kind of like base or limiting distribution at t equals one so that's kind of just a small kind of and so the idea is to construct you know to district the data with a continuous series of noise and this would be formalized by by a stochastic differential equations which we could which you know people like have introduced as like the for nosing process because you'd like continuously nose and destroy data samples until like you know time capital t. The idea right is that that would converge to a known distribution such as like a question and then the core idea of diffusion models is like oh but you know since we have this forward nosing process going from like you know the data distribution to a known distribution can we you know simulate from the time reversal starting from like this known data distribution to samples are like distributed accordingly to the data distribution that we care about. So in a question one have like kind of like a white class of SDEs with a drift term in B and like a you know volatility or diffusion coefficient in sigma and in question two we have a specific type we refer to as like the long run dynamics and what's kind of nice here is that the long run dynamics admits as like an environment measure something that's proportional to exponential minus u with u you know I guess being some kind of like energy potential and so you can kind of choose u so that you know towards what this stochastic process is going to converge and so if you take like a specific example of b just being zero so you know if you use like constant then b is going to be zero then you just have simply this like brain motion term and then the environment measure would just be the the low bank and another kind of like indeed like very well studied example is the oceanic process with a drift is linear in the in the viable here whitey and here if you choose kind of the coefficient white wisely then you the environment measure would be a standard normal with zero and and violence one and what's kind of quite nice is that conditional like a data sample why zero for any time t we we we explicitly know the form of the conditional whitey given why zero and it is Gaussian and with minimum parameters depending on like why zero and you know the value of the noising time t yeah and so I guess I kind of didn't put that slide but for people who are more familiar with like this and like discrete diffusion models you can see them as like discretization of the of these SD's and kind of vice versa so the kind of region in this way yeah so like some future interpreter there's like any question or something that's unclear or some remarks also I'll give some I'll leave some time at the end for that but you know yeah if there's anything so is that really clear okay so then what's nice so what we care about is this time reversal you know when we sort of time index like you know what what is the dynamic of that process and what's nice is that under like very mild assumption it has been shown that this time reversal also satisfies an SD's that has like a pretty similar form which is given here where the diffusion term sigma is the sum the only difference is in the drift and so where you have kind of like the opposite of the forward drift of the noising process drift so you can see that you know if sigma is zero basically there's just like the time reversal of an OD but here since we have some noise sigma then you have this extra term in yellow which is referred as the Steinskull and I guess intuitively right so here pt being like the density of the forward process so intuitively that tells you you know what is the kind of the direction of of you know the of the the density at time zero at time t sorry and so it kind of tells you you know where in some way you would like to go in order to get closer to the datamin for especially as t gets close to zero so that gives us like a recipe like how to you know to build a generative model by you know starting with some yt bar so weight weight sorry y zero bar so being the kind of initial value of the time reversal so kind of the last value the end value of the of the forward process and then if you were to be able to simulate that then you at time you know t equal one you'll get simple that distributed according to the data process which is what you know that's kind of the aim I guess there's you know obviously a few kind of like issues which prevents us from doing that the first one as mentioned earlier is that actually we don't have access you know to y capital t which is kind of the end time of the the diffusion we have access to like you know y capital t given some you know specific y zero but not to the marginal so I guess the idea is that you know if you run t long enough and indeed specifically if you use like like long run dynamics this would converge with like geometric rates actually converges like pretty quickly so it would be approximate but you know you know it can be like relatively close to actually start with this you know invariant limiting distribution then the other kind of issue maybe the main one is that we don't we don't have access to the science goal because it would involve so solid focal length and so we don't want to go through that through that avenue so the idea is to learn it and that's what we that's what I'm going to show in the next slide how to do that and I guess the the last issue is like even if we were to know the science goal apparently it has like a very simple functional form like you know linear in in whitey then there's no way we're going to be able to solve that that sd in closed form and so the idea then is need to like discretize it yeah so how to indeed like approximate the skull so there's like kind of like a pretty simple skull matching identity has been shown which involves just like you know derivative of the log and then just using the fact that the you know marginal is the integral of the joints and and basal and you can show a question three and what's nice is that the right hand side of question three is a conditional expectation with the integral being the first term and so that tells you that the the left hand side of the equation is the minimizer of that following you know like quadratic optimistic pressure optimization problem over the functional s and so so that kind of readily gives us a you know a loss to you know to use in order to approximate the Stein score s and so the idea is to use a neural network with some you know finite number of parameters theta and to plug our score network in equation four which is called the denoising score matching loss and since we you know we know what is yt given y0 we have that in closed form can both compute this conditional score but we can also you know some you know approximate this expectation with like a Monte Carlo estimator and so finally we would you know this sd wouldn't be plugging the score network instead of the true Stein score then you know one could rely on like a first order discretization scheme for instance in order to approximate that sd and then starting with like the you know here for instance like white noise so Gaussian noise on each of the pixel and then using this Euler-Maurier scheme in order to approximate simulate the time reversal one could sample you know obtain simple that are approximately distributed according to the data distribution okay so the idea of continuous addition models like quick recap is I guess like kind of two things one is to build construct a a nosing process that's formalized with like an sd in order to continually destroy the the data sample and then to approximate the time reversal process okay so is there is there any question on that part because I guess then I'm gonna build on that so okay so now I'd like to I guess introduce the diving into the the method that we proposed this probabilistic model of a feature field and so that's that's kind of the the aim of of this talk and so like briefly and I'm gonna mention that I introduced that a bit more formalized just after like tensor fields are like mappings from like an epic space to a tensor and tensors are like geometric object that transform in the particular when the the coordinates change so you could think of like nuclear in space like a rotation or translation of your frame and we like also to enforce in violence for such objects over with respect to group transformation and we can have achieved this in two ways first by extending different models to the two function space by creating the finite marginals for different inputs and then by enforcing uh in violence indeed using a kernel and a score network which are a group equivalent so I'll go more in details how we do so in practice so a few examples first to motivate like why do we care about feature fields in the natural sciences there's like many problems where it kind of like objects of interest of study are feature fields so one can think of like a case you know in oceanography where the sanity is like a scalar field or you know current is like a 2d or 3d vector field but also like the vorticity you know in the atmosphere being like a pseudo vector field which is slightly different but also in mechanics with like for instance a high-dimensional ring to a strength tensor which is also like a tensor just an objects like high-dimensional and so more rigorously so a feature field can be seen as like a tuple with first a mapping f you know mapping some input to like some multiple space which we assume to be a cleaning here and some representation row which kind of tells you like what type this feature this like tensor that is mapping from like a group element to a linear transformation here over all the and so focusing on like the Euclain setting if when the group being like the Euclain group which is like all the other matrices of the Euclain space so think like indeed like translation and rotation so then acting transforming a feature field with some group element g on f of x so the feature field f with you know g being like u a translation composed with a rotation h then it's going to be it's going to transform the field as given by the right hand side of equation five which is that first I guess if you look at like the top left picture this is the original feature field then you would kind of like look at the value of your feature field at g minus one of x and then you would update your transform that feature field according to row of h and here row depends on the specific types of field that you're like interested in modeling so first kind of field a row would just be one so actually you wouldn't change anything so that's why the middle and right hand side plots I can have exactly the same in terms of the the colors but if you look at like the the black arrows so that's like a 2d vector fields then you would additionally row would be row of h would be h would be the identity mapping so you would add additionally need to like rotate these arrows and that kind of you know concept of representations I can have generalized to like higher dimensional tensors you know higher dimension than like vector field for instance this stress tensor in mechanics and so what we want to do is like to build a probabilistic model over search objects and so first I'll show how we can extend different models to the function space and so the idea is to basically if we look at like different value of our functions at different inputs x1 to xn so we have answered values is to know is all of these values like jointly with a kernel that's given by hk and so then you have like for instance like a multivide austen and back process that are nosing each of these values with this this kernel k and so that's why you have this gram matrix k of xx in front of the brilliant motion and what's nice is that so I guess this is similar to kind of the finite setting because you're only looking at like a finite number of inputs x1 to xm so you know that this process will converge to a multivide normal which mean and covariance are given by m and k and we show that basically since it is true for any finite marginals in practice is building a diffusion models of a function space and so the process will converge to a Gaussian process with min and kernel given by autos of m and k and so you can see why t has like interpolation you know between stochastic processes why zero being like the data process that you're trying to model and why infinity being the kind of this this gp at the kind of infinite time limit and same here everything is kind of like available in close-up everything's Gaussian so that's that's very convenient for training purposes so depending on the specific choice of kernel then the process will converge to different gps here with like you know a smaller length scale and I guess you know in the infinitely small length scale then you would converge to white noise so I guess here you actually know each of the y xk independently yeah yeah yeah that's like a modeling choice I guess depending on you know what you may know about the the data process and I guess same with the mean but I guess you would typically assume zero constant yeah so that's like um yeah so that's like up to you and so yeah kind of then we similarly have like a time reversal process and the only difference is that we have this like gram matrix k in front of this goal but otherwise you know it's kind of like the same the same ideas in the as previously and that time reversal we like you know converge you know we start with a gp so why zero bar is being of the gp and then the idea is to simulate the time reversal so that adds a y bar a capital t then we we have you know sample path our distributed according to our data process y zero and so we don't have access similarly to the steinskull so we have to approximate it and since we have you know the steinskull is preconditioned by this gram matrix we can have suggest to directly learn this with the neural network and same the the conditional yt given y zero is available in closed form and so what you know we also use this denoising converging loss and we kind of use a weighting that involves this gram matrix so that it's kind of multiplying both you know both terms of inside this quadratic term with k so then we have like k times the score that appears and we can directly approximate this with the neural network and then also on the right hand side it's nice because we don't have any like k inverse we just have like I guess we have like a actually it gives a composition to do and that's that's the training laws that we actually use okay so yeah is there is there any questions on this okay so now I'll go into like the invariance kind of considering that we like to enforce in our neural diffusion process and so I guess a stochastic process f that comes from a priority measure mu is said to be gene-variant if the the measure itself is gene-variant and so maybe in terms of like sample it's easier to understand is that if you have some like input pairs you know x and f of x and then you have like this you transform the same set with a group of m and g and then those two sets would have the same distribution if the stochastic process is gene-variant so that's that's the definition so you can think of like in 1d if you have like you know samples a simple path then if you're just kind of like translates that entire distribution then if f is translation variant which is often refers like this is stationary then the samples pass will have the same distribution so that's kind of the property that we like to enforce in our model and what one specific and kind of like simple example of this is an invariant gp and as I have been sure that like a gp is basically gene-variant if and only if the mean and the kernel are g-equivalent defined as in equation 9 and so that implies that basically the mean is constant and for the kernel it's kind of like a bit trickier and like I'm giving some example in the Euclidean setting so you can the simplest idea is just to take k being like k0 times some identity matrix and k0 being group invariant so for the Euclidean group you can think of like you know like the lvf or the mat and kernel for instance but they have been like suggested like more interesting and so I guess this diagonal kernel you can see there's like here the x and y component on the left hand side being independent gps so I guess you have two kind of scalar gps but you can also use like you know kind of more interesting kernels which will lead to the you know sample vector fields on the middle and the right hand side which have zero divergence and zero curl and so this satisfies this g-equivalent constraint and so they they lead to like g-invariant gps so I guess what what you like from here is like how can we enforce that group invariant invariance constraints into our generative model over you know function space and so what we show is that you need kind of two things so you need to indeed target you know have as this limiting process a group in variant gp and so as as just shown you can do that by having a group equivalent mean and kernel and then the other constraint is for this core network to be also group equivalent and so that can be done since we are like parameterizing this core network with like a neural network we can basically choose the specific neural architecture to enforce that and so depending on the type of field that we modeling you know like a scalar field you know that we want to be translation variant or you know vector field or even high dimensional objects we can just choose a neural architecture that enforces this group equivalent with respect to this type of field and so here's like a bit of an illustration of of what that means in practice in terms of samples so on the most left hand side kind of column we have samples from this you know in variant gp so this is what we start this is like kind of our white noise and here we have like a an rbf kernel and in the bottom row this is basically the sample that's above but that is transformed by a 90 degree rotation and then the generative model you know applies this denoising scheme by approximating the time reversal with s core network that is that is here e2 equivalent and so what you have when it is fully denoised at time equal one is that the sample at the bottom is still the 90 degree rotation transform sample at the above and so that's because both samples have the same distribution they kind of the same apart from this like 90 degree transformation so I guess in practice this noise we should like this group invariance over like kind of stochastic processes but in practice what we are often interested is more like the in the conditional setting you know if we have access to like some set of observation and we like to be able you know conditioning of this observation you know sample from the conditional process you know could be for like I guess as the previous example from like weather forecast you like have some data from like you know weather stations but then you'd not like to know the precipitation like different space time kind of point and so then the property that's like kind of more of interest is this more like group equivalence which is given here which is like you know if you were to transform your context set the set you're like conditioning in you'd like your conditional process to also be transformed in a similar way and so I guess here there's like an illustration in figure five for like a 1d kind of scalar field example if you translate those three red dots with which we are conditioning on by like two then the conditional process which similarly be transformed so all the sample path in blue are you know also translated from the left to the right and then you know another example on the right with like a 2d vector field where similarly the the transformation is like a 90 degree rotations so the context sets being like here like these red arrows and then you know we're like transforming the context sets by like a 90 degree rotations and then the conditional process is similarly transformed and so then the question would be like how can we you know how does this you know is enforced how can we enforce this similarly to these you know gene vines of the you know unconditional process and basically what can be shown is that this kind of two sides of the same comes they are actually the same thing so if your stochastic process is gene-vined then this conditional process is going to be G coins so it's going to like it comes so free so and we've shown how to how to enforce our stochastic process to be gene-vined by having like a G coin channel and a G coin score network so then in practice we'd like to be able to get samples from that you know given like a context set so we calling up the context set C and you know given some like cray points that we're interested in like X stars we'll be able to sample those like Y stars and so they have been proposed in the literature by in this paper by Brian Tripp et al kind of like a relatively and I guess also by Yang Tung relatively like simple scheme which which involves kind of two things one being to noise the context so that what we are you know YC what we want to condition on until you know this level of of noise T that we are looking at at the moment and then then we apply like a denoising step you know we'd like some discretization step gamma on both the context YC and the viable we're interested Y star and then the idea is just to repeat this until you're like kind of fully denoised your your samples and then your what can be shown is that as gamma tends to zero or with like an SMC creative step you have samples that are like kind of exactly distributed according to the conditional process but what we've upset in practice is that you really need gamma to be extremely small and I guess the smaller gamma is the more steps you're taking and so the more like calls to your network you have which is obviously that's kind of like the the commercial cost that that you know scales linearly and so you like to keep a gamma pretty pretty large as large as you can and because if gamma is like not small enough then basically empirically this we tend to dismiss the context to sample from the unconditional process and so in the literature by Fugumal et al in this repaint algorithm which is kind like this because it was looking at like image and painting so you know you're like high part of the image and you like sample from from from that there's this kind of simple yet nice idea has been suggested which is to add for like a given level of of noising tea to add some like renoising and denoising kind of steps in order to increase the correlation between y star and y y c and that indeed like actually works well in practice and so what we what we show in this work is that this is actually a discretization scheme of long run dynamics and I guess yeah maybe I don't have too much time to dive into this but you can indeed of this kind of repaint corrective step you can simply do a large amount of mix in order to get your your at fault so I guess in if it is this we have an illustration of this so the predictor step which is the one from the previous slide would be this blue arrow where you're kind of like making a guess you know trying to remove one level of noise and you know approximate that but you know because it's like approximate your discretizing things wouldn't be exact so you'd be kind of off shooting the kind of true trajectory of your denoising process and then this long run corrector would be this kind of this red arrow that's kind of like you know projecting you back in some sense onto this you know the true the true level you know and true your your true kind of conditional density of your stochastic process and what we show what we observe kind of empirically is that you really need to do some long run steps because if you have zero on the y axis you have some kind of measure of how good you're like fitting this conditional sample and you need to do at least a couple of long run steps to to yeah to match that efficiently um okay so yeah is there any any question I guess I'll I guess tell like a few minutes to go through some experimental reasons that we there we have so first we looked at like 1d kind of datasets so which you can see here in the first column uh where the first three are our Gaussian processes um and the last two are like non Gaussian uh and so then we train our model along like other uh baselines that I'll share in the next slide and here you can see in the middle column like just samples from the kind of you know unconditional uh process and then on the right column right inside column uh samples from the conditional process and then we measure the predictive log likelihood uh and what we can see is I guess this this model like is able to uh you know fit both Gaussian and non Gaussian processes as opposed for instance to like null uh like some different class of neural processes which uh you know we will relatively like very well on on Gaussian processes but maybe not as well on non Gaussian processes for instance on like this so to make sure that I said um and then on the on this bottom row we do the kind of the same thing but instead of like evaluating within the training range we evaluate like outside of the training range yes yeah so I skip all of this in a because of time constraints but so I guess we basically just computing the log of the joint so of both the variables you know y star and yc and then we also computing the log of the conditional set and then we subtracting this too and you can yeah basically you can compute the probability flow of of you know the joint and this uh yeah it's kind of pretty similar otherwise on the regal um yeah and so here you know models that basically have no uh kind of like translation invariance uh constraints you know wouldn't be able to like perform like in any way uh well outside the training range um but uh such stationary neural processes such as comms gp or gnp uh perform as well and similarly for uh this uh the model in blue it perform as well in the you know outside the training range um but this n dp star hey is like is exactly the same model as the one in blue just does not have this translation uh in variance constraints in this core network and it's basically like performed like really badly that's why we didn't even report the results because kind of it's never been trained on that range so there's no reason so anyway so that shows that basically this this model like works well in Gaussian and not Gaussian settings and can generalize having this like stationary assumption so then we look at uh 2d Gaussian vector fields uh so with the candles that I've mentioned earlier and uh similarly we can see this n dp star does not have so neural diffusion processes does not have uh a group of covariance uh constraints and it works pretty well nonetheless uh but not as well as the one in blue which you know we we use like uh an e2 equivalent uh parameterization for this core network where it's kind of like you know matching exactly the predictive likelihood of the datasets and I guess most may be useful is that uh you know although they perform both like relatively similarly like the equivalent uh stochastic process uh in orange can perform very well with like extremely few data points uh as opposed to the other one which which has like requires like way way more data points to be able in some way to kind of learn this invariance property uh because the orange one kind of like works on like a state space that's like way lower dimensionality so you kind of like giving these heads down it's kind of massive heads down and yeah no that's that is true yeah uh no that's that's a fair point yeah it's really we should do that yeah yeah I mean it's true maybe this yeah this is maybe a bit unfair you're right uh I guess yet typically you would add some determination uh that's that's a good point so I guess yeah it wouldn't be as dramatic uh and very yeah lastly I guess okay I've very skipped that but basically here we show we look at like cyclone trajectories uh which are like you know we model as like being sample path uh you know functions from like r to s2 so r being the time index and s2 being like a position on earth and we we model this this this data set so you can see on the bottom left um like samples from the data sets on the bottom right samples from our train model and we basically built on like a prior work where we extend different models onto the manifold setting um anyway I guess I'll skip this but uh yeah as a quick recap uh make sure everyone's kind of like have the the key ideas is that we the aim was to build a probabilistic model over uh tensor fields or like you know collection of tensor fields feature fields um and so first we extended the different models over function spaces by correlating you know all the finite marginals with some kind of kernel and we also showed how we could incorporate a group invariant and that's with two things one by targeting an invariant Gaussian process by using a a group of covariant mean and kernel and secondly by uh parameterizing this core network with also a group of covariant neural network uh and then also I showed like that the long joint character was really key to be able to effectively sample from the functional process and uh then yeah I guess I showed like a few a few examples on how like this class of models because as opposed to like neural processes doesn't have this diagonal uh predictive covariant structure uh it's able to like really have like really good modeling capacity on both like scalar vector fields and with processes with uh euclidean and spherical uh output uh yeah thanks for your attention uh yeah I guess if we have a bit of time for our remarks that's uh lovely and uh also yeah definitely looking forward to uh uh chat with you during this weekend to meet you yeah which this one or this one yeah I guess I'm wondering how I see so in the so you would you would still know is the context but you would uh simulate the reverse probability flow or d in this step yeah that's a good point okay I guess yeah so yes yeah so what would be kind of too big because in practice like I mean for the problems I showed after like even like a couple of thousands of steps wouldn't work so well so that doesn't sound like a massive amount of step of noise injecting each time but maybe it is still too much indeed like that's that's a good point yes yes yeah yeah so adding large one but no but we did actually uh try both uh that's a good point indeed like reversing the probability flow or the the time result and uh indeed the probability flow works a bit better but still like with no kind of corrector like it's still like you know so that's that's a good point I think that it does help but still like with no corrector basically you get samples are like yeah basically very much not really conditioned on the context side so they look good but they kind of like tend to slightly ignore the context set uh yeah more questions yes so on the score network um so yeah I guess indeed I guess in the literature this shows that although it kind of like in some way already telling was the kind of right function class then it seems that it may have an impact on like the optimization program so it may be harder uh so for the 1d setting it's kind of in some way we didn't change the architecture we just kind of remove the center of mass of the delta so that's like a just very simple trick to get a translation variant network and for the second one we use like a like a kind of s2 transformer kind of architecture so it's like a a graph base with like an attention kind of idea uh so I guess a bit hard to because yeah I guess I'm not sure how I can specifically single out that you know it's not easy to train because of this specific because of the equivalence for me the issue was more like the fact that because it's like a graph base architecture like uh it scales like doesn't scale so well with the number of of you know inputs that you want to model your stochastic processor um so yeah I didn't really notice like let's say like instabilities or thing like this was more like instead of like memory scalability uh but I guess this is more like maybe the specific choice of like graph architecture I guess maybe I like smarter choice I mean if you work on the grid then you yeah you wouldn't need to do that but I guess the idea right of this stochastic process is that you'll be able to evaluate them anyway yes I'm at the follow-up question would be do you think the extra I mean and that's the question we always ask on those with extra work seems to actually be worth it and in your case maybe you can comment on how much extra work you have to do to make it equivalent compared to turning it on the augmented data I see and I guess then do you see that you are actually doing better yeah so I guess history I didn't try which is maybe a bit a bit unfair and silly but you did know in your slide you compared to the non-equivarian version oh yeah yeah but I guess they didn't try like uh like with data augmentation which maybe would be a bit of a more fair comparison that is true uh but no so it does definitely I mean I guess here like I mean obviously since you're not trying this kind of network on this kind of border modularization like outside of this kind of minus one one range there's no way it can work if you don't enforce like session it but it was like other and neural process like also can do this like convolution kind of based neural processes but also if you have like a gp that is stationary you or something of that so here's like really like just wouldn't work at all otherwise and hey it doesn't really require any just like yeah removing like the center of mass that's like doesn't really change anything so that's like very easy yeah so here I guess yeah it it does require like kind of like knowledge and has about how like to work with equivalent neural network which are a bit more tricky yeah trickier because I guess you need to know a bit more about like uh representations and yeah how to implement them and such things so yeah I mean it's hard it's hard to tell I mean it's true I didn't value it on like data augmentation but at least with all that like it does you know I guess you're like cushioning out like a big part of the state space uh so I mean these are a bit toys something I'm working on uh is like working on like more like weather data with like wind direction and like temperature and pressure fields so I'd be interested to see like how well that works and hey maybe this environment constraint would be too strong because if you have you know if you're like modeling a specific you know if you in some way you kind of discard you know this is land this is like sea but uh I think you could soften the covariance by amortizing this core network this search kind of like topological I mean like like geographical data so you kind of have like a soft equivalence but yeah so that's something I'd like to try I haven't really tried so yeah I don't think it's is necessarily worth it for every problem because it could be seen as like a pretty strong constraint in some instances but others like you kind of know that you have that on it's like partially you have this and so then I think it does make sense because then you can generalize better and train with less data any more question so if not I suggest we thank again the speakers of this session


Seven steps to help you develop unstoppable confidence to help you succeed at anything.  Do you feel like you're just not sure of yourself and it's stopping you from doing certain things that you would just love to do? Well, you're gonna want to watch this video to the very end because I'm gonna give you seven things that you can do to change all that. I mean change all that to have that confidence you want to achieve the things you want. All you got to do is these seven steps. And the other thing you got to do is you got to subscribe. There's a button down there, right? It's a red button if you haven't subscribed. You need to click it turn it gray and don't forget turn all bell notifications So you're notified each and every time I go live. Okay, guys Let's get into this. Are there things that you want to do, but you just don't feel confident doing them. Now, I will tell you building confidence is a gradual process. It's not gonna happen overnight. I remember, you know, being much younger and being very Unconfident and being very nervous and worrying about what everybody thought. It really stopped me from doing a lot of things that I wanted to do. So in this video, I want to help you out with that so you don't have that same issue. So let's let's start with the first thing that I just said and number one is stop comparing yourself to everybody else. It really doesn't matter what everybody else is doing. You are not in competition with everybody else. You're in competition with yourself. Okay, that's the only person you need to worry about. And I think a lot of times people see other people achieve things or do things and they compare themselves to them and they're like, Man, I need to do that. I need to be that. I'm not all that. That's not what you need to do guys. You need to compare yourself with yourself and as soon as you stop comparing yourself with everybody else, you're gonna see your confidence level increase. Number two, we get misdirected and I'm gonna say number two is the loudest is not always the most confident and I'm gonna say it that way because I think a lot of times we see people who are loud and always talk about themselves and always brag about themselves as being the most confident people and we say gee, I don't want to be confident if I got a brag about everything I do and quite honestly over the years and I've been around for a few years now, I've noticed that the people that are the most confident are the most humble people. They're the nicest people. They don't talk about their achievements. They don't talk about what they've done.  All they do is they walk a confident walk and all of their actions speak for themselves. So don't worry about having to be somebody who's got to be real boisterous and always talking about yourself and real loud and all that. That's not confidence. That usually is lack of confidence. Number three, be aware of your limiting beliefs. Limiting beliefs stop people from doing so many things. I know when I started selling online, I was selling products online and all that. One of my big limiting beliefs was that I didn't have any technical knowledge. I didn't know anything about technical stuff. I didn't know anything about computers. I could even send e-bails. I would mess that up. When I found the reply button in the forward button, I thought I'd really done something. So I was not technologically advanced. I couldn't type and to sell stuff online. I remember a lot of my friends and family would say, well, you're going to sell stuff online. You can't even type. How are you going to possibly do that?  So you've got to get away from that. You don't have any limits at all. There are no limits. Only thing you have to do is just keep trying new things. And as you keep trying new things, you're going to get better at new things. I know now that I can do some coding. I can build webpages. I have sold millions of dollars of products of both digital and physical products online. And this is from a guy who can't type and doesn't know how to send e-mails. So if I can learn these things, guys, you can learn them too. Stop your limiting beliefs. Stop telling yourself you can't do something because of something else. Just do it. It doesn't matter what you've heard the past. It doesn't matter what you thought of yourself in the past. It doesn't matter what other people think of you either because they have their own limiting beliefs. What you want to do is just move forward and keep trying things even if you don't feel confident. I think a lot of people say, well, I'm going to try that item once I start to feel confident. That's not how it works. What you do is you try those items and you get some small successes and you start to feel more confident. And then you're able to try more of those items. That's how you would approach it. Number four, live positively. What do I mean by live positively? Well, I think a lot of people tend to look around and look at negative things. We were having a conversation the other day. My wife and I and I think I was complaining or she was complaining about something. But then we compared it to the people in Afghanistan and this is during 2021 if you're watching this video at any other time. And there were millions of people that just wanted to leave the country. They wanted to have a fair shot at being in a place that gave them opportunity. They were handing over their children to soldiers in hopes that their children would end up in a better place. You've got to be positive. There's no point in complaining even if you're right.  What's the point in whining like a baby? One of my favorite people to listen to is David Goggins. So my self-talk is like no one else is self-talkable. So this is what I say to myself. It changes all the time. But let's say I'm training for 100 mile race and I get to mile 50. And I feel like **** and like everybody else, my mind gets soft. Why? Because I'm human.  No, not human. I suffer. I don't like it. I'm uncomfortable. I don't like it. So my mind starts to get weak and we start to forget about how badass we are. This is about self-talk. Most of us never start anything cold.  If you want to run 100 mile race, a marathon, if you want to go be Mr. Olympia, if you want to go be a scientist, a doctor, what happens is in that moment when we need self-talk, when we're failing and we're in our worst spot possible, we forget about the front end, all the build-up to where we're at today. We forget how much work we put in. So for me, we forget that we put years, years, maybe not into making these dials, but to getting where you're at today, to become this person, to be in a position to make this money. We forget that journey on what it took for us to get in this moment to make the right decision. So that's my self-talk is this, okay, I'm going to get the **** out of here, man. I'm done. Now remember this, you ran 2,000 miles training to be in this moment right now. We forget that. We forget the three o'clock in the morning runs or getting up early for work or whatever you're doing. We forget all that in that moment of suffering. I remind myself. And look him up. It's David, G-O-G-G-I-N-S.  It doesn't matter if it hurts. It doesn't matter if it's difficult. It doesn't matter. Just go do it. Just be positive and do things. Think positive. Talk positive. Do positive things. It will make a world of difference in your confidence. Number five, embody confidence. Now, here's what I mean by that. You can't wait to feel confident to do things that a confident person would do. What you have to do is embody confidence. Tell yourself you're confident. Tell yourself that you can do things. Feed yourself good positive energy. When you tell yourself you're confident, you're going to start to feel it. When you tell yourself you're not confident and you're scared and you're worried, you're going to start to believe that too. Understand, these are all just thoughts.  They aren't real. None of this is real, okay? What you try to do is based on your confidence level, but your confidence level is based on what you're feeding your own mind. Tell yourself that you have great capabilities. Look back on your past successes and remind yourself of all you've already done. Think about it. When you were a baby, you couldn't talk. You couldn't walk. You couldn't do anything. Look at what you've already accomplished. Give yourself some credit. Stop. Give yourself some credit and you're going to gain some more confidence. Number six, don't dwell in the past. Now, I have seen many people for many years try to change the past. Not one of them has been successful. Not a one of them has been successful. Your past is just that. Your past is not your future. Your past is just the past. That's all it is, okay? It doesn't define who you are today. I mean, if you think about it, there's been so many people that had really, really difficult beginnings that became wildly successful or did horrible things and overcome those horrible things. That is not your future. You can't control the past. There's no point even thinking about the past, really. You can't control it. There's nothing you can do about it. You certainly can't change it. However, you can change the future. And that's a positive thought you need to have. You need to think about what you can do in the future to make things better. And as you realize that you can actually change the future, you can change maybe you've been on a bad path, maybe you've been on a losing path, but there were reasons for it. Maybe you didn't put forth the effort. Maybe you didn't try hard. Maybe you were negative. Whatever the reasons, you can change all of those things. You can start today. Today can be the first day. Don't put it off till tomorrow. Today can be the first day that you start thinking positive, doing positive, getting in shape, working harder, putting in the time, turning off the TV, the radio, all that stuff, and actually putting energy into your family and your life and your work. Today can be the day to do that. That's how you need to think about it. Number seven, be committed. Be committed. Now, if you ask the average person how many books will it take for you to read to learn how to, I don't know, put a spaceship in orbit. How many books would you have to read? And the person may be like, gee, I don't know. I don't know. That sounds pretty difficult. Now, I remember not too long ago, Elon Musk, who was the founder of SpaceX, the founder of Tesla, the founder of the boring company, so many different things, was on 60 Minutes. And they were interviewing him. And the interviewer said to him, how in the world could you put these rocket ships up in space? You're not a rocket scientist. You're not an astronaut. You're not any of these things. How could you possibly do that? And he had one answer for that interviewer. And the answer was, there's books. There's books. So, if you take somebody who's super positive and you ask them how many books will you read to accomplish a particular goal, how many courses will you go to, how much training will you take, their answer is going to be whatever it takes. In other words, they are committed. They're going to do whatever it takes to accomplish that goal. Is that your attitude? Is that your attitude? Do you have the attitude that you're going to do whatever it takes to accomplish that goal? If you're trying to run a marathon, will you get up at four in the morning or three in the morning? Will you run two or three times? Are you going to do what it takes when you run two or three times a day? What will you do? Somebody who is fully committed is almost impossible to beat because they won't stop. They just won't stop. That's the attitude that you have to develop. And I don't care about your past. I don't care about your childhood. I don't care about your parents. I don't care how much money you got in the bank. You can change that attitude today. Or you can use all those previous things as crutches. I had a pretty tough childhood. I won't get into it on this particular video, but for all intents and purposes, I should have been in jail. I should have done some terrible things, but I chose to leave that behind. Are you going to choose to leave all that stuff behind to become a more confident person after watching this video? Are you going to choose to put these things into practice and actually do them? Or is this just another video you're going to watch and move on to the next one? Hopefully, you're going to make those changes. And I want to hear about those changes. I want to hear what you did. I want you to go through these seven things I gave you in this video, and I want you to put in the comments, what did you do? What did you do? Or what are you going to do? Sometimes just writing down what you're going to do, this will help you. Put it in the comments below. Hey, JR, I heard what you said about being committed, and I'm going to start being committed. I heard what you said about being positive. I'm going to change my self-talk. I'm going to change the verbal talk that I'm talking to other people. I'm not going to complain about stuff anymore. I'm going to start realizing and being grateful for what I've got. Guys, being confident is all of these things. You really can't be super confident without doing every single thing on this list. All seven of them. Don't skip any of them. Don't skip any steps. If you were going up a flight of steps, you said, I'm going to take one step at a time, but every now and then I'm going to take four, every now and then I'm going to take three. If you started skipping steps, what's going to happen? You're going to fall. You're going to get hurt. It's not going to work out too well for you. Don't skip any steps. Use every single step that I've taught you in this video. Apply them, and you're going to be a more confident person. Once that's happened, come back to this video and post in there how confident you are and what you have achieved. Now, you have other questions, and this is a little bit off of selling online because that's what I do, but you have any other questions about selling online, making money online, you can put those in the comments section below. I often will come through. I'll answer those and I'll help you out. I want to thank you so much for watching this video. I really appreciate it. Don't forget to subscribe. Hit that subscribe button down there. If it's red, turn it gray, click it, turn it gray. Don't forget to ring the bell. Turn on all bell notifications so you're notified each and every time I go live. Thank you so much for watching this video. I really appreciate you, and I'll see you in the next video. Hey, thanks for watching my video. Don't forget to subscribe to my channel and click that little bell right here so you can be notified every time I do a new video. Also, click on one of those videos there. Keep watching on my channel.


Hello everyone that is on Okay, no sleeping. I saw that yawning over there, but it happened before we started talking.  We promise But but if these questions aren't the ones that are keeping you awake feel free to jump up to one of these microphones We want this to be interactive Because we are your community organizers We we first we were at scale I think when we first thought about this this talk and because we all get a lot of questions I'll let everybody introduce themselves in a second But you have the people who bear who run the three largest user groups in the world between San Francisco with over 55,000 hundred fifty-five hundred members five thousand five hundred La has about a thousand Seattle has a well over a thousand So large user groups and I even checked I looked at China I looked at a bunch of the other ones that I like no we really we are the largest so We have built these communities and and sustained them and kept them going for for years all of us And we're gonna share with you a little bit About how we did that Also, we're going to cover a bunch of other things questions We guys asked all the time will cover, you know some definitions some kind of address some community responsibilities We'll cover budget because that's always a fun one and some of the tools and resources that are available to you that you guys might not know About and some content related things. So With that why don't we have folks introduce themselves? I'll start at the end Sean Hi, Sean Roberts. I work for Walmart and I'm part of the San Francisco user group To Zuluca course from Blue Box and IBM company and I am the Seattle user group leader Oh Gary Kivorkian, I am with Cisco on the events and marketing team and I run OpenStack LA and I am Lisa Marine Amphi everyone calls me Lisa and And Sean and I have been running SF Bay for probably four years from when you started I'm I am currently at Hewlett Packard. And so he'll pack it to enterprise. Sorry and SF Bay user group and the online user group Okay, so I think we'll we'll start out with a definition. There's there's a lot of confusion between user groups and meet-ups and so It's a topic or a little bit passionate about so why don't I actually have Sean kick off this topic? Okay, so There's a It's been easy in the US to use User groups synonymous with meet-up because that's a tool that a lot of people use it's an easy way to get a lot of Eyeballs and get more people showing up that aren't typically part of the OpenStack community But there's quite a bit of work groups outside of the US that don't actually use meet-up and some that actually don't even have access to it so We just thought it was good just to at least bring it up that I purposely try to Say user group as much as possible rather than the shortcut meet-up because it confuses people so OpenStack user groups are not one-to-one with meet-up so Because anybody can just go on meet-up comm and create a meet-up But that doesn't necessarily mean you would be a recognized OpenStack user group There is actually criteria on the opensack org Foundation website to become an official OpenStack user group and so we want people to go down that road and The other thing Sean and I deal with in San Francisco in the Bay area.  It's kind of the same concept We are stronger together. There should be one meet-up site so for instance SF Bay area includes the East Bay and San Francisco and We encourage those organizers of those other groups to join us and to be organizers of our group and to have one calendar It's so much better for our users So that happens in other areas to really try to resist having that happen Even if you think you're an hour away a drive in traffic It's just still a better thing to use one meet-up comm site for your community user group but it's in the way that It's taking a little while to get the steam going but groups dot open stock org exists to Organize the the groups and that give Everyone a place to go that centralized that's not specific to any one tool and if there's many groups It would have a tendency that people wouldn't know where to go and don't understand the difference And it just confuses them when they're trying to learn so last thing we want to do better together community Okay, and so some of the tools that we rely on to build this community You know, we don't love meet-up comm but we're kind of married to it And so hopefully if anyone wants to that should be open source and people should be able to improve Meet-up comm and that'd be an awesome idea. It may disagree just a tiny bit, but Sure, so one of the things that in fact We're talking about this is ambassadors, which is I'm not sure if everyone knows what an ambassador is But it's basically the the most active user groups get typically a little bit of extra help from the OpenStack foundation There's about 12 ambassadors right now. They're basically just very active user groups. So but the conversation is about it'd be ideal if actually from Groups that opens back at org if it was capable of being the source of truth for new events And it would actually we'd be able to have it send information out to meet up in G groups and Facebook and all these other different places that people are used to using So that it's that's we can get more people more people involved more people know about meetings I guess you wanted to talk a little bit about some of the other tools Developer, yeah, we're gonna talk about you know the importance of things like github or IRC But I don't know that necessarily that after now having been sitting through a lot of sessions this week I don't know that we actually need to go there on the last day of the conference I think everybody's pretty been inundated with with the technical tools that it takes to build this community So the the main tool is groups that open stack to org. So if you if you all go there and Check it out, then you'll be able to find all the events no matter where they're Hosted whether it be Facebook or meetup Okay, so I'm gonna ask Gary and Tisula some questions now because maybe Tisula can start because she's the most Organized of the of the four of us up here. So why don't we address, you know budget? Like how do you run a meetup if you have zero budget? So you make somebody else pay for it very simple very simple You ask them it's a it's a pay-to-play situation you ask for sponsors and you allow them to bring their swag you allow them to Mention their company at the end of the presentation. It can't be a sales pitch But you do give them a little bit of freedom to show who they are because they're buying dinner that night and And they'll provide the logo. They'll provide a lot of support to you in return I started running the LA open-stack meetup Sort of getting thrown into the deep end the the previous organizer was leaving LA And was looking for a successor so he handpicked me so every Thursday night Or every fourth Thursday of the month. I'm busy and my wife is not that thrilled about it but some of the challenges that I found were That budget issue, how do you do it where it's no out-of-pocket to you as an organizer? No out-of-pocket to your people that come in but you still give them great content You still give them a reason to show up every month And one of the things that I learned and actually dealing with HP early on Was a lot of the companies that we work with and have had come in actually have community outreach programs And you can find the right people within each like for example, I'll use HP as the example that Not only did they send a presenter? They sent you obviously paid for dinner in the pay-to-play model. They sent swag They sent a raffle gift. They sent down a laptop to be raffled off to my group at the end of the session And you just have to learn to ask I've had at OpenStack LA probably in the last eight months. I've had Mirantis. I've had Red Hat. I've had Mitokura Avi Networks big switch every single one of them willing to come in pick up the tab for dinner pick up the tab for drinks come in give a great presentation mingle with the group and It it's a lot easier than you think you just have to learn to get past the point of being Afraid to ask and that that was actually my biggest challenge was trying to figure out where you go Who to find and then make the ask and though there are more than willing to come down and help sponsor the meet-ups Yeah, I think for us. I actually see it a little bit differently We have we meet twice a month and although in the last month and a half It's been almost every week and I don't want to discourage that that kind of Enthusiasm and and community participation and when someone comes to us with a topic a really good topic Or if I'm doing something completely upstream like we had Adrian Otto speaking on Project Magnum I asked him to come I always will ask ptls to come and they're not going to come and represent You know he didn't talk about rack space So I'm you know he didn't pay for that one and one of the criteria that I like to use to have people join our group as Organizers is you have to be willing to host meet-ups and to pay for content that is upstream and that come from other Companies that don't necessarily I mean for instance that HP also I don't really have a way to cross-charge people for the security For the beer, so I've always paid for those those things So now and I was lucky, you know Sean did a yahoo the years before that and I've done it for the last two years at HP And I was lucky that I actually had a budget that I could leverage Since we had our company split and we don't have as much budget for things like that anymore I have had to get more creative about it So we try to move it around we had one at IBM a couple weeks ago There there was one at Walmart a few weeks ago We had our next one is going to be at the VMware campus So if you move it around that way you can also share the love and the expense and get somebody else to pick up the tab Well, I was I was actually we may get to it in a second But I'm going to get to the moving it around issue, but we'll we'll get there down. Okay Logistics and to seal is the queen of logistics. So I'm also going to Queen keep talking Lisa Yeah, so logistically I am the one that that like kind of took the reins and have not have not stopped Refining my program to make my life easier. It was purely selfish but I had when I first came in I also took it over from someone else and There was it was one of those things where it was a labor of love before I got it and it was great And it was wonderful, but there was no actual plan and since I'm a marketer I like to have a plan so I made a plan and I made a checklist of what every speaker is going to need when they come in and Everything from the type of equipment that they're going to bring to the flash drive back up of their presentation that they're going to have To where I'm going to want them to stand when they get to IBM So it's been really helpful for me to send that out to their event organizer Whoever I'm liaisoning with to have them fully prepared by the time they get to me And I also have them send their presentation in advance So I can review the slides and make sure they work on our system because we're going to plug them into our system Our AV system and to make sure that they're not too hyper promotional, but we'll save that Go ahead and want to just dive into that one right there. Yeah, okay one of the things that I It took me a while to get accustomed to was being able to tell a presenter that what they want to come and do is not right Because I anybody who has been to you know, especially something like an open-stack meetup The developers the coders the the guys that are running open-stack in businesses Are gonna run like rats when you turn on the light in a garage if someone comes in with a real sales he presentation So it took a while to be able to help You know, even as I reviewing their presentations just like to Sue does to be able to have someone tone it down and I actually periodically have to moderate a presenter when they sort of Send me one thing and then show up with something else and you have to get you have to sort of get them back on track and You know, I probably sort of segue into where we're going with content and what we like to see But you know my sort of stuff is under the hood lessons learned You know take a technical deep dive on something show the people show the attendees something They're not used to seeing and taking them sort of behind the curtains and showing them really what's making open-stack Tick and what's making it easier for them to deploy in their businesses Yeah, so I would just add one of the things it's early on when I was at Yahoo And I was working to build a culture around open-stack at Yahoo And that was my ulterior motive. Also, we needed somebody to run that the group since the person that was was moving on Did I organized a bunch of hackathons and it was actually we focused a lot more about Riding code and how to commit so I trained a lot of people One of the things we did is we tried to find people jobs. It was you know, it was pretty much just gorilla open-stack so Then as time went on and things kind of evolved a lot more We started bringing in all the local PTLs or core reviewers Like we've had recently. We've had Mike Perez. We've had Adrian Otto We've had bunch of different people who are fortunate that these people are in the area So if you do know Yeah, people well, that's definitely true But even better to help build the local community that if you know that you have Core reviewers doesn't have to be a PTL, but or somebody who is a really active contributor in your neighborhood You know ask him to speak, you know do it as much as they can they feel comfortable doing it people really like that it is all about the content and we used to Toggle between having beginner sessions and advanced sessions kind of that's why we were doing it every other week And it just got too complicated to try to do it that way But now we just really publish what the content is going to be ahead of time So a couple weeks ago We had a couple of cores from Keystone come and speak and it was a very very technical Discussion and so we even gave our user group homework before we're like go to this website is because we knew You know if you were a beginner or if you were just there looking for a job You know this was not going to be the meetup for you and and every time before we do a meetup I stand up and I talk about what the next five meetups are going to be so people do get to Know what's coming ahead and on that one especially I was like this we're going to go deep on this one It's going to be very very technical and so many people came up to me after that meetup And they were so grateful for that level of content So I would say you know go as deep as you as you can I mean that's people are in this industry because of the technology and so Don't be afraid of the of the seriously deep technical dive. Maybe not every time but um And then just let your community know that that's what's going to happen Um, I have a few more questions, but if there's any burning questions from the audience Uh, we can take those or not burning questions any Small any sizzling questions Oh, no, no questions yet. Maybe I should start asking the audience questions So, um I see somebody from uh, hp sysco IBM you're also hardware sellers. I mean you said don't let a vendor go specific Into their product, but what if you wanted so okay, del has here's their solution Well, here's and have somebody from hp and give their architecture solution or any of the other I mean from randas comes in, you know, they're going to be talking about fuel Do you find that that's okay or if as long as they people know up up front Yeah, I I didn't usually tell the presenters, you know, keep your keep your slides community oriented upstream But obviously if you're going to show a demo, it's probably going to have your company logo Somewhere on that demo, you know and and and if the demo is as interesting technology and it is relevant to open stack We get a lot of requests for things that shawna. I have to say what what does this have to do with open stack? But if it's relevant for open stack, then of course, that's fine and the swag thing too at first Um, when I first started hosting at hp I was a little bit careful about just passing out swag because I didn't want it to seem like it was a big marketing event Um, but after the community was coming over and over again, and then was trusting us Turns out they love swag. We've given away a lot of t-shirts a lot Yeah, there's those glowing cubes. My kids love them. So they much appreciate them the way I look at it is when I run my meetup. I'm gary the meetup organizer. I'm not gary from sysco I run I run it independent. I run it independently. That's why I don't have an issue with Badges off is the yeah one of the exactly it's a completely neutral environment and anybody's welcome to come in As long as it makes as long as the content makes sense um, I just wanted to Circle background on the on the sponsors thing, you know, um, I run the one of the user groups in the uk and um, you know, we so far over the last What is it 18 months two years? We've pretty much had a stable set of sponsors They're not quite as generous as the sponsors in the us by the sounds of it. Do you guys have um A stable set of sponsors or are you rotating them for each event depending on who's speaking? I would say they rotate just depending on who's willing to give the money on that day um, I we don't tend to always tie it to speakers, but um, and I wouldn't say it's stable they've But you know, we've been running our user group for five years And so it's a revolving door of different people all the time and you know We've had marantis in there in a big way in the past and they're not so much anymore And and we've had, you know swift stack and we've had a lot of great companies, um, sysco So it kind of goes like this a little bit and sometimes you have to pass the hat around there's a way of fixing that. I'm I'm I'm trying to work. I had I had this kind of air cover from my finance department when I was at yahu Um, so they they cover pretty much anything I wanted to a large extent. So that got me spoiled Um now that I don't necessarily have that anymore, um, but I have a version of it at walmart So I'm I'm training a finance guy to be Um, essentially my banker Um, and I I want to do it actually for all of us in the west coast. Um, so The idea is yeah the idea is that we um, we all Basically dole in the money and it gives us some breathing room in between. Um, you know the the peak the valleys but also, um Lisa heads um with each most HP's help, um, and some really great, um, event organizers organize some awesome, uh, birthday parties in the past But the problem with doing that is that it's in a massive amount of work to gather the money and to get everything paid So just running that through HP is a nightmare and makes it undo burden on her As opposed to putting the burden back on the team. So, um, so I'd like to do that Partially so that we could when we do have these birthday party events or larger events that we can Build up the slush fund to support that ahead of time. We actually know the budget It's not You know something we have to go asking for people afterwards saying, you know, you said you're gonna pay could you please pay now? so, um, so just just that so Yeah, and we'll um, so we have about six more minutes. Um, we will take this question We'll also talk about some of the um the the tools available to you and we didn't really address things like Burnout, you know, one thing I would say is you need multiple organizers of your of your community user group And I mean you said your wife is not too pleased with the frequency I think we're all married to open stack really if we want to know who our first wife is It's been a labor of love. And if you don't absolutely love this community, you will burn out really really quickly But just trying to spread that love around has been, you know, really Essential to to not being completely burned out all the time. Um, but okay, let's take this question I was just curious is this These user groups, are they a show and tell or do you also get into kind of a brainstorming? Let's work on a common problem that matters to the community So, you know, it's not like one company or, you know, any one presenter Presenting and maybe a Q&A at the end, but it's like, you know, the whole audience is the panel Yeah, this this is actually something that happens almost every single meetup that I host That we have a longer Q&A than presentation I actually have to kick people out sometimes because our elevators lock at a certain time and we do not want to be staying there for all night But yes, when when the presentation is over and the speaker asks for questions And there are definitely plenty of questions directed to the speaker But you will also find that there are plenty of questions questions directed to each other because people say Oh, I'm from here and I do this and I do this This gentleman at my last meetup last month literally showed up and just said my open stack is broken help And like he had like six people running over to him during cupcake time, you know to go to go help him So it was great. We have cupcake We have a kegerator and cupcake You have food and a Seattle beer in Seattle You need to go to Seattle But yes, that that happens very often organically. It's not something that we honestly plan, but it definitely always happens I mean another scenario. I mean a different kind of scenario, but yeah, I think to Sula's point every meetup turns into probably a longer discussion than presentation and The other thing that I've tried a couple of times and has worked out really well is just as opposed to a single presenter Getting a group of my regular Participants and we do a series of lightning talks Everybody gets 10 minutes at the mic and That that also becomes a little problematic because the 10 minutes goes by very very quickly and the q&a goes by very You know generally wants to go longer than the presentation itself. So Um, you know, that's another thing to consider too because the one thing I've learned after a lot of years in tech Is that people in tech have no shortage of ability of talking about what they know about so But you have to they're also shy I mean this is you know, we've done it both ways But we find if we have at least 10 minutes of prepared remarks and I mean the beer helps too, but It does and when that second beer kicks in people get a little bit unshy less shy But it helps to have some sort of prepared Thing at the beginning just to get that conversation started But I mean, I was just going to add to that that you know that that interactive part of it I mean for us certainly is as important as the actual speakers I mean, you know, we we try not to pack I mean we kind of do two two speakers per event But we specifically have you know a big chunk of time there There's just for people to drink beer and talk to each other because you know that's half the point That's the community part of it So one of the things Kind of pitched something that we've done before so it's it's part of the upstream university that Stefano and luik started a few years ago and it's now On the front end of every single conference. It's just super awesome So I snagged a little part of it the the lego activity. I don't know if anybody's sat through that, but it's super fun And it requires a lot of legos about $2,000 us of legos But I've done it like five times now. I did it at a university that I work with on And I've done it a few other places and we we did it last year At the opens.com Our user group as well For those who don't know it's it's this fantastic game that helps you Understand how to participate in this community upstream called the upstream legal game or something like that It's not really a game But it's a way for people the lego is really just a tool that's being used to have people Understand how to work together and I mean just really quickly Why don't you explain just the three groups and because you guys should take this idea for your meet-ups? I'm sure it's and it's actually on slide slide share as well But it did the basic ideas that it teaches at adult agile agile method agile methods So you break up into a couple teams to create a project and it's essentially creating a building That's going to be attached to another building Um, and you have some people that rove and you have some groups act as companies But the um, and you go through a whole build process a couple cycles of it But the the whole point of it really is to not only teach how open stack works in a unique way without actually writing any code um, but it it It's like a hackathon without code and that's really How I got started getting in uh, that getting involved with the community And actually I really want to bring it back. I'm getting to the point where um, I can have enough time to bring the hackathons back So we can actually have a set time Twice a month where people just know if they show up this place There's going to be some guys working on code and um, they can help with the commit they can Talk about a particular feature that's getting pushed in a project. That's um of interest and you know, we just get that set schedule down, but um Anyway, that it's a lot of fun I'd recommend it if anybody Wants to reach out to how it's done. It's it's pretty straightforward, but um, and I'd be actually if somebody Wants I could actually even fly out Walmart is more than willing to have that happen. So Um, I've run it as long as four hours, but it can be a little bit less We did it in about two and a half that night So this is what we did for our fifth birthday party So there were cupcakes and you probably saw some pictures of it scrolling through on gary slides. Yeah, the the biggest um The the well the biggest thing is the the amount of legos. So I have I have six huge boxes of legos My kids love it actually um, so And then you know You could if you wanted to if you wanted to do it over again You could a region could maybe pitch together get a couple sponsors to help them buy the legos and then user groups could share So we've actually talked about doing that for the east coast united states Yeah, so in the spirit of sharing content one of the things Sean I've been talking about for years But we really love to to get it implemented this year It's a way to share the content that we do out of the bay area with the other groups around the world And so um, I think you started this with the training when we were kind of cycling through the the eight weeks of the open Stack training that you can find on the docs open stack dot org But we we do want to have a way to share the slides to you know Because presenters will fly out and and if you if you make the request But you guys are very talented as well And it's sometimes it's just content that people want And we do know I mean we do One of the questions we didn't get to was do we google hangout or do we not google hangout? Like we have to google hangout because we have people from all over the world that that pipe into the sf bay one Because of the content But we want to find a way to share that content with the other user groups So we're going to work on that this year and if you have ideas then ping us It's just out of curiosity. How many of you have ever been to a meet-up or a user group for open stack? Good. Okay. Good. Good. Has anyone not? Ah, okay. Okay. We're what area are you based in? Ah, okay. So Doug Helman ran that one for a long time with Keith burger and uh, yeah, it's pretty robust. They're awesome Yeah, but they're looking at doing um In uh, what is it called Buckhead or Bucktown? Buckhead is Buckhead. Yeah, okay So I think the maybe the red hat office there is going to start hosting it. I don't know if that's closer to you But ping Doug Helman. He's a great guy and He runs a great community and you'd be surprised how many are out there So if you live in a place that's not a real big city like where all of us live Just look for it and you'll probably find it I'm out. I'm curious. We're in the uk area We're in the uk You got a pretty you got a pretty big community. Yeah, I mean Manchester's a big tech hub. So, you know, there's a lot of a lot of people there and A lot of people working for people like red hat stuff from home. So, you know, and the uk is not that small So, you know, lots of contributors who are living in the uk.  So congratulations. Who else runs user groups community organizers? Okay, give yourselves a hand because wow, it's hard. Yeah Seriously, good job. Well done. Where's your user group? South Florida, okay, and philly. Oh, I want to go to the philly one. What a great town Not that I don't want to go to south florida, but That's always awesome, but philly. We should do another we were going to do the operator summit in philly last year We didn't do that. What happened Who's mid-cycle the operation was in philly that was two years ago. Yeah. Yeah, because we was in Palo Alto last year I decided not to travel we spend a lot of time working out of suitcases Okay, so any other questions because I think we're at the end of our time Okay, we go to I don't know We're the last that we could just keep you all night. So in meetup style, we should have brought beer and pizza What what what did we do the one thing I the one thing I'd like and I made notes only because so I don't leave stuff Well, she's got a book. I just have a piece of paper torn out of a notebook. So I'm actually more impressed with her That there are a lot of resources available to organizers that a lot of people just don't know about a lot of them directly through open stack Um the ability to post your event on their event page um What shan was talking about a couple of minutes ago was the difference between a recognized And I don't I don't want to call them independent but there's there's a new process that open stack is instituted to Get your group recognized by open stack that makes you eligible for additional things that they can help out with You can get into the swag store and buy open stack stuff to have it your to have at your group Um, they have a little stipend in the month of july Which is the open stack birthday month and recognize groups get a small chunk of cash very small Well, I well this shirt is from my july from my july birthday meet-up last year. Um Um, and I think one of for me and again going back to when I first took over the group Finding people to come to your group to do present presentations Is they've actually just now created sort of a rudimentary I'll call it a rune entry speakers bureau where you can actually go and find resources And look at people's profiles. Look at their presentations and actually help You find the people that can come help It's No, it's it's actually i'm trying to remember now where it is Is it is it okay, but yes, and it's it's an alphabetical listing and you know This came up in a discussion when we were in tokyo and it was a community meeting there And I I brought it up that that was my biggest challenge as a meet-up organizer when I first took it up was finding people to come in And you know, I'd like to see it maybe even elevated to almost like a yelp status where the you know We're an organizer of a group like myself could actually rate the presenter to help people make decisions Is it in that is okay, that's right Um, and the other thing I would just say I mean, I know we're all not thrilled with meetup.com as Our basic tool, but I think for me one of the things that I have found real useful is The ability to look at the other groups that my members are involved in by looking at their profile Because that's a great way to help cross promote your events with the other groups create sort of an extended community between You know, I've got open stack la. There's devops la There's a container group in la and now we're all sort of starting to work together to You know cross promote our groups and our events that is super super important You know the we had a we did a cloud sling meet-up the other day And so we had the docker group promote that because we were you know spinning up Apps with you know in containers in core os and so take your content outside of just open stack I mean as as we're all going up the stack now with we've mixed our cloud foundry meet-up with open stack A couple of times and you get a nice cross pollination of communities and that will keep your community going The mobile version won't let me see it Any any other questions you have yeah, so the one so we in in philly we got in the rut of One or two presenters every time from some company kind and and one of the first things we've been looking at doing is the lightning talks And uh, we're looking for more ideas for hands-on stuff the lego thing is sounds awesome. Um, And we're trying to find some other smaller hands-on stuff like labs because we get at least in philly we get some contributors, but a lot of just More on the operator side So we're even trying to look at like maybe labs or something like that that we can go through even for end users So have you seen any good resources of other things you can do at a meet-ups besides talks yeah, so Yeah, oh it's cool. So, uh, so what we started off doing is very similar. Um, so When citrix is still involved, um, we got into this and I'll probably like four and a half years ago now, but um We got into this rut. It was a good rut for a while where, um ewan meller who is running the that user group at the time and um and a few other people and myself uh Would just give like basic open stack talk over and over and over again And then some people started showing like yeah, you know, I've seen you talk on this four times Can you not do that anymore like do anything? You know, um, so what we started doing is, um, we tried to break it out so that The newcomers would be able to go to one talk. In fact, I was for a long time at yahoo I was holding two talks at the same time which Like almost well it hurt me for a while because it was a lot of work to do it But I'd have um one group that was all the newcomers Would we give the the pre-can talk and then we'd have the hackathon in the other room And so everyone that wanted to hack code go off in the room when we'd break up into smaller groups and Usually about half of those people wanted to actually understand how to contribute And then some others were would kind of show up and ask python questions And then there was a smaller group that would break off and work on some particular problem Like they'd grab a bug off of the queue Off a nova or something and just toy with it So it was usually not super advanced the hackathon, but it was it was generally people getting together to learn so so Maybe something along those lines the so it's the reason why I started the training project Training guides it's still out there. I'm not involved anymore because I kind of moved on and there's another team that's supporting it But it that actually the activity of starting the training guides project started a whole bunch of other things So I would recommend either getting involved with that and starting to use it and maybe contributing to it And that could be just the activity that you do out of the user group just that in itself That would help you understand how projects work better on a fundamental level An alternative would be to maybe hold some python training sessions Like test driven development on python. There's like four or five really good resources and you could kind of hold python training With the intent of contributing to open stack because tdd is a really good way of learning python Um, I don't know just a couple ideas sure You alluded to this before but um, and maybe there's some guidelines Somewhere on groups, but what's the best? We don't have any money yet, but we've had offers What's the best way to handle finances? Do you get you don't bank account for the group or cash in a box or We could probably give four different answers So I I don't touch their money ever I send them a list of Restaurants that they can order food from in my neighborhood and I tell them You know send me what percentage you want me to tip if you're not there to to greet the driver But we will not be paying for this if you don't we will cancel the order Etc. Etc. Like hands off because our accounting team can't deal with that sort of thing It would be a nightmare But you always pay for the beer and I always pay for the beer we always we always take the hit for the drinks Yeah, of course, but We're usually a couple hundred bucks Those open stackers they drink a lot. No, I pretty much work the same models to sue I I don't want to see a credit card number. I don't want to see cash Um, I would say maybe the one exception to that is when you do the open stack birthday celebration They'll pay you out via paypal account And then then then there's that whole Transition to how you actually spend it and yeah, they'll ask for your I just connected them with my accounting team for our birthday last year And again, I didn't touch any money that I bought cupcakes and stickers Yeah, but it is funny though. I mean I've been on the phone with people and they're like, do you want my credit card number? Well, yes, I do, but you know not for why you want to give it to me Um, but yeah, so I generally try to stay away from that as much as possible and just let them take care of that And honestly, that's one less thing I have to deal with And all I know is that at 6 15 the food for the meetup is going to show up it's taken care of And yeah, the other part of that is the person who is presenting probably is as Distanced from that part of the process as possible. So it just makes life a lot easier See and I just I guess like to make life really hard But um, I take the credit card number because I want control of that whole thing because we have a big facility We have security people that we have to deal with we have to escort the caterers in and out It's just a lot of logistics that that we do deal with and we don't want the presenter worrying about it So in the few times that I have taken money for somebody else to host on our site It's just been the credit card number for the pizza And then we call it into pizza my heart and then I give that person the receipt at the end of the night So, yeah, it is pizza. We didn't meet these ones They have a gluten-free pizza. So we stick to pizza.  I'm gluten-free. So I can't do the beer or pizza.  And uh, bear was nice enough to Broadcast that I think we have similar stories to tell about stuff that's now hidden in desks I will say nothing So and I mentioned before that, uh, I'm training one of our finance people to be my banker So, um, with the intent to take this burden off of all of us because it's a pain um, so I was lucky enough when I was at yahoo and I got it started that, um that uh It was they were very very flexible. Um, it allowed me to do all kinds of things. So I I could pay for pretty much anything Um, it's not as much the case and I technically don't want to actually deal with the numbers and the details either because it's Um, a finance person is much better at that. So I did some of that before Um, I wouldn't recommend it. Um, so anyway So if you can if you can start training your own finance person to uh, say hey, would you love to take this on? It's going to help the business, you know How many people do you have at your meetups? I mean 15 people you can also just go to a restaurant or major to bar Yeah, and then everybody kind of pays for their own and that's most meetups started that way Awesome. Yeah, you'll grow fast trust me. I I took over la open stack and we were 400 members And that was just two years ago and we're approaching the thousand now. So you there is a big demand for knowledge and I don't know where how yours works, but I usually get about 50 percent of my regulars And then about another 50 percent of just newcomers each time just wanting to learn more. So It'll grow trust me It is a lot of fun, that's the most important thing have have fun with it It's a community one of the questions we didn't get to is like what do you do with the heckler in the room? How do you manage, you know crowds and yeah, so we and we can have fun conversations about those things later When we have our next round of whiskey, right? Right But there's all sorts of tips and tricks and and things once it grows and you start You know, we have about 100 people that come to our meetups pretty pretty regularly So it's but it's important to that's that's the community. That's the whole point So you got to embrace it all all the quirkiness all the strangeness Diversity was another thing. We didn't really get a chance to talk about really really important to have your presenters be a very diverse You know women people of color people of different, you know Gender identifications. That's probably a seattle and portland thing more than in other parts of the world But have you been to LA? True. That's right. Yes. Well, Mike Perez used to run yours. So everybody feels comfortable At the LA meetup But anyway, that's super super important for I have a lot of women come up to me afterwards Or they ping me before saying are you going to be there because like I'm going to go if you're going to be there Like, you know, you made me feel comfortable in this community And and we're trying to grow our our female population and do things like women of open sex scholarships And and I talk about things like that at our meetup And it really helps to include everyone Can you? Yeah, Ken is awesome. He's he's a good guy Okay Oh, well, I know but you know, we're here for you. So I gotta go to my working group that I'm waiting for Speaking of ambassadors. He was supposed to be in the ambassador working group thing right now But okay, thank you everybody. Thank you everybody. Thank you for coming