Tuesday, November 8, 2016

Tutorial: Categorical Variational Autoencoders using Gumbel-Softmax

You can find the code for this article here



Gumbel-Softmax Distribution



TensorFlow Implementation



Categorical VAE with Gumbel-Softmax

First, we declare the encoding network:


Finally, we run train our VAE:





Tuesday, September 6, 2016

Riemann Summation and Physics Simulation are Statistically Biased

Riemann summation is the simplest method for numerically approximating integrals. in which you chop up the domain into equally sized segments and add a bunch of rectangles together.

$$\int f(x) dx \sim  \sum_i^k f(x) \Delta x$$

This technique also comes up in Euler integration for simulating physics:

\Delta x & = \int \dot{x}(t) dt \\
& \sim \dot{x}(t) \Delta t

These are all special cases of "Deterministic Quadratures", where the integration step size $\Delta x$ is non-random ("deterministic") and we are summing up a bunch of quadrilateral pieces ("Quadratures") to approximate areas.

This post interprets the numerical integration as a special case of stratified sampling, and shows that deterministic quadrature rules are statistically biased estimators.

Suppose we want to compute $E_X[f(X)] = \int_x p(x)\cdot f(x) dx$ via a Monte Carlo estimator.

A plain Monte Carlo Estimator is given by $\mu = \frac{1}{n}\sum_i^n f(X_i)$, and has variance $\frac{1}{n}\sigma^2$ where $\sigma^2=Var[X_i]$.

Stratified sampling (see previous blog post) introduces a stratification variable $Y$ with a discrete distribution of $k$ strata with probability densities  $p(y_1),p(y_2),...,p(y_k)$, respectively. A common choice is to assign each $p(y_1) = p(y_2) = ... = p(y_k) = 1/k$, and arrange the strata in a grid. In this case, $y_i$ correspond to the corners of the strata and $P(X|Y=y_i)$ corresponds to a uniform distribution over that square ($X_i = Y_i + 0.1*\text{rand}()$).

The variance of this estimator is $\sum_i^k p(y_i)^2 \cdot V_i$, where $V_i$ is the variance of $\mathbb{E}[X|Y=y_i]$ for strata $i$.

Suppose we let $\mathbb{E}[X|Y=y_i]$ as $y_i$ - that is, we just sample at the corners where $y_i$ sampels are.

The good news is that $V_i = 0$, and we've reduced the variance of the total estimator to zero. The bad news is that the estimator is now biased because our per-strata estimators $\mathbb{E}[X|Y=y_i]$ are biased (our estimator never uses information from the interiors/edges of the squares). The estimator is also inconsistent for finite strata.

Does this figure remind you of anything? This estimator behaves identically to a Riemann Summation in 2D!

What does this mean?

  • If you ever take a Riemann sum with fixed step size, your integral is biased! 
  • If you are monitoring mean ocean temperatures and place your sensors in an regular grid across the water (like above), your estimator is biased! 
  • If you are recording video at a too-low frequency and pick up aliasing artifacts, your estimates are biased!
  • If you use a fixed timestep in a physics simulation, such as Euler or Runge-Kutta methods, your space integrals are biased!

The takeaway here is that *all* deterministic quadrature rules are statistically biased. that's okay though - the expected value of biased estimators *do* converge to the true expectation as you crank the number of strata to infinity (e.g. $\Delta x \to 0$).

A "less-biased" physics simulator ought to incorporate random time steps (for instance, sampled between 0 and MAX_TIME_STEP). To reduce variance, one might generate multiple samples, compute a nonlinear update, then average out the integration.

Fairly obvious in hindsight, but I found it interesting to think about the relationship between stratified sampling and deterministic quadrature :)

Monday, September 5, 2016

Monte Carlo Variance Reduction Techniques in Julia

Monte Carlo methods are a simple yet powerful family of techniques for performing numerical integration in high-dimensional spaces.

This post is a tutorial on a few variance reduction techniques that can be used to reduce the noisiness of your estimators: antithetic samplingcontrol variates, and stratified sampling.

Code on Github

The Problem

Our goal is to find the expected value of the random variable $X$:

$$X = f(u) = \exp(\sin(8 u_1^2 + 3 u_1)+ \sin(11 u_2))$$

where $u$ is a 2D point sampled uniformly from the unit square, as shown below:

f(x,y) = exp(sin(8x.^2+3x)+sin(11y))

The expectation can be re-written as an integral over the unit square.

\mathbb{E}[X] = \int_0^1 \int_0^1 \exp(\sin(8 u_1^2 + 3 u_1)+ \sin(11 u_2)) du_1 du_2

Monte Carlo is useful in scenarios where a closed-form integral does not exist, or we do not have access to the analytical form of the function (for example, it could be a black box program that the NSA hands to us). In this case we can integrate it analytically, but let's pretend we can't :)

Basic Monte Carlo Estimator

The basic Monte Carlo estimator $\hat{\mu}$ gives a straightforward answer: "evaluate $x_i=f(u_i)$ at $n$ random values of $U$, and then take the average among all your samples." (to make comparison easier with the estimators I will introduce later on, I use $2n$ samples instead of $n$).

\hat{\mu} = \frac{1}{2n}\sum_i^{2n} x_i \approx \mathbb{E}[X]

Here is the estimator in code :

function plainMC(N)
    u1 = rand(2*N)
    u2 = rand(2*N)
    return u1,u2,f(u1,u2),[]

The plot below shows 100 samples and their values, colored according to the value of $f(X)$ at that location.

        title="Plain MC Samples",

The variance of this (2n-sample) estimator is given by:

\Var[\hat{\mu}] & = \Var[ \frac{1}{2n}\sum_i^{2n} X_i ] \\
& = (\frac{1}{2n})^2\sum_i^{2n} \Var [X_i] \\
& = \frac{\sigma^2}{2n}

Where $\sigma^2$ is the variance of a single sample $X_i$ and $n$ is the total number of samples.

The variance of our estimator is $\mathbb{E}[(\hat{\mu}-\mu)^2]$, or in other words, the "expected squared error" with respect to the true expectation $\mu$. 

We could decrease variance by simply cranking up our sample count $n$, but this is computationally expensive. While variance is proportional to $\frac{1}{n}$, the standard deviation (which has the same units as $\mu$ and is proportional to the confidence interval of our estimator) decreases at $\frac{1}{\sqrt{n}}$, a far slower rate. This is highly unsatisfactory.

Instead, we will use variance reduction techniques to construct estimators with the same expected value as our MC estimator, and still achieve a lower variance with the same number of samples.

Antithetic Sampling

Antithetic sampling is a slight extension to the basic Monte Carlo estimator. The idea is to pair each sample $X_i$ with an "antithetic sample" $Y_i$ such that $X_i$ and $Y_i$ are identically distributed, but negatively correlated. Our single-sample estimate is given by:

\hat{\mu} = \frac{X_i + Y_i}{2}

$\mathbb{E}[X_i] = \mathbb{E}[Y_i]$ so this estimator has the same expected value as the plain MC estimator.

\Var[\hat{\mu}] & = \frac{1}{4}\Var[X_i + Y_i] \\
& = \frac{1}{4}(\Var[X_i] + \Var[Y_i] + 2 \text{Cov}(X_i,Y_i)) && \Var[X_i] = \Var[Y_i] = \sigma^2 \\
& = \frac{1}{2}(\sigma^2 + \sigma^2\beta) && \beta = \text{Corr}(X_i,Y_i) = \frac{\text{Cov}(X_i,Y_i)}{\sigma^2}

If we sample $N$ of these pairs (for a total of $2N$ samples), the variance is $\frac{1}{2n}(\sigma^2 + \sigma^2\beta)$, and it's easy to see that this estimator has a strictly lower variance than the plain Monte Carlo as long as $\beta$ is negative.

Unfortunately, there isn't a general method for generating good antithetic samples. If the random sample of interest $X_i = f(U_i)$ is a transformation of a uniform random variable $U_i \sim \text{Uniform}(0,1)$, we can try "flipping" the input and letting $Y_i = f(1-U_i)$. $1-U_i$ is distributed identically to $U_i$, and assuming $f(U_i)$ is monotonic, then $Y_i$ will be large when $X_i$ is small, and vice versa.

However, this monotonicity assumption does not hold in general for 2D functions. In fact, for our choice of $f(U)$, it is only slightly negatively correlated ($\beta \approx -0.07$).

function antithetic(N)
    u1_x = rand(N); u2_x = rand(N);
    u1_y = 1-u1_x; u2_y = 1-u2_x;
    x = f(u1_x,u2_x);
    y = f(u1_y,u2_y);
    return [u1_x; u1_y], [u2_x; u2_y], [x;y], (u1_x,u2_x,x,u1_y,u2_y,y)

Notice how each $X_i$ sample ("+") is paired with an antithetic sample $Y_i$ ("x") mirrored across the origin.

u1_x,u2_x,x,u1_y,u2_y,y = ii
        size=(400,400),label="X samples",
        xlabel="u1",ylabel="u2",title="Antithetic Sampling")
        label="antithetic samples")

Control Variates

Control Variates is similar to Antithetic Sampling, in that we will be pairing every sample $X_i$ with a sample of $Y_i$ (the "control variate"), and exploiting correlations between $X_i$ and $Y_i$ in order to decrease variance.

However, $Y_i$ is no longer sampled from the same distribution as $X_i$. Typically $Y_i$ is some random variable that is computationally easy to generate (or is generated as a side effect) from the generative process for $X_i$. For instance, the weather report that forecasts temperature $X$ also happens to predict rainfall $Y$. We can use rainfall measurements $Y_i$ to improve our estimates of  "average temperature" $\mathbb{E}[X]$.

Suppose control variate $Y$ has a known expected value $\bar{\mu}$. The single-sample estimator is given by

\hat{\mu} = X_i - c (Y_i - \mathbb{E}[Y_i])

Where $c$ is some constant. In expectation, $Y_i$ samples are cancelled out by $\bar{\mu}$ so this estimator is unbiased.

It's variance is

\Var[\hat{\mu}] & = \Var[X_i - c (Y_i - \mathbb{E}[Y_i])] \\
& = \Var[X_i - c Y_i] && \mathbb{E}[Y_i] \text{ is not random, and has zero variance} \\
& = \Var[X_i] + c^2 \Var[Y_i] + 2cCov(X_i,Y_i)

Simple calculus tells us that the value of $c$ that minimizes $Var[\hat{\mu}]$ is

c^* = -\frac{Cov(X_i,Y_i)}{\Var(Y_i)}

so the best variance we can achieve is

\Var[\hat{\mu}] = \Var[X_i] - \frac{Cov(X_i,Y_i)^2}{\Var(Y_i)}

The variance of our estimator is reduced as long as $Cov(X_i,Y_i)^2 \neq 0$. Unlike antithetic sampling, control variates can be either positively or negatively correlated with $X_i$.

The catch here is that $c^*$ will need to be estimated. There are a couple ways:

  1. Forget optimality, just let $c^* = 1$! This naive approach still works better than basic Monte Carlo because it exploits covariance structure.
  2. Estimate $c^*$ from samples $X_i$, $Y_i$. Doing so introduces a dependence between $c^*$ and $Y_i$, which makes the estimator biased, but in practice this bias is small compared to the standard error of the estimate.
  3. An unbiased alternative to (2) is to compute $c^*$ from a small set of $m < n$ "pilot samples", and then throw the pilot samples away before collecting the real $n$ samples.

In the case of our $f(u)$, we know that the modes of the distribution are scattered in the corners of the unit square. In polar coordinates centered at $(0.5,0.5)$, that's about $k \frac{\pi}{2} + \frac{\pi}{4}, k=0,1,2,3$. Let's choose $Y = -cos(4\theta)$, where $\theta$ is uniform about the unit circle.  

cv(u1,u2) = -cos(4*atan((u2-.5)./(u1-.5)))
pcv = plot(linspace(0,1,100),linspace(0,1,100),cv,st=:contourf,title="Y",xlabel="u1",ylabel="u2")

\mathbb{E}_U[Y] & = \int_0^1 \int_0^1 -\cos(4\theta) du_1 du_2 \\
& = \int_0^1 \int_0^1 - \frac{1-\tan^2(2\theta)}{1+\tan^2(2\theta)} du_1 du_2 \\
& = \int_0^1 \int_0^1 - \frac{1-(\frac{2\tan \theta}{1 - \tan^2(\theta)})^2}{1+(\frac{2\tan \theta}{1 - \tan^2(\theta)})^2} du_1 du_2 && \tan(\theta) = \frac{y}{x} \\
& = \pi-3

In practice though, we don't have an analytic way to compute $E[Y]$, but we can estimate it in an unbiased manner from our pilot samples.

function controlvariates(N)
    # generate pilot samples
    npilot = 30
    u1 = rand(npilot); u2 = rand(npilot)
    x = f(u1,u2)
    y = cv(u1,u2)
    c = -β/var(y)
    μ_y = mean(y) # estimate mean from pilot samples
    # real samples
    u1 = rand(2*N); u2 = rand(2*N)
    x = f(u1,u2)
    y = cv(u1,u2)
    return u1,u2,x+c*(y-μ_y),(μ_y, β, c)

We can verify from our pilot samples that the covariance is quite large:

for i=1:1000
    βs = [βs; ii[2]]
mean(βs) # 0.367405

Stratified Sampling

The idea behind stratified sampling is very simple: instead of estimating $E[X]$ over the domain $U$, we break up the domain into $K$ strata, estimate the conditional expectation $X$ over each strata separately, then average our per-strata estimates.

In the above picture, the sample space is divided into 50 strata going across the horizontal axis and there are 2 points placed in each strata. There are lots of ways to stratify - you can stratify in a 1D grid, 2D grid, concentric circles with varying areas, whatever you like.

We're not restricted to chopping up $U$ into little fiefdoms as above (although it is a natural choice). Instead, we introduce a "stratification variable" $Y$ with discrete values $y_1,...,y_k$. Our estimators for each strata correspond to the conditional expectations $\mathbb{E}[X|Y=y_i] \text{ for } i=1...K$.

The estimator is given by a weighted combination of strata estimates with the respective probability of each strata:

function stratified(N)
    # N strata, 2 samples per strata = 2N samples
    u1 = Float64[]
    u2 = Float64[]
    for y=range(0,1/N,N)
        for i=1:2

\hat{mu} = \sum_i^k p(y_i) \mathbb{E}[X|Y=y_i]

The variance of this estimator is

\sum_i^k p(y_i)^2 (V_i)

Where $V_i$ is the variance of estimator $i$.

For simplicity, let's assume:

  1. We stratify along an evenly-spaced grid, i.e. $p(y_i) = \frac{1}{k}$
  2. We are using a simple MC estimator within each strata, $V_i =  \frac{\sigma_i^2}{n_i}$, with $n_i = \frac{2n}{k}$ samples each (so the total number of samples is $2n$, the same as the plain MC estimator). $\sigma_i^2$ is the variance of an individual sample within each strata.

Then the variance is

\sum_i^k  (\frac{1}{k} p(y_i)) (\frac{k}{2n}\sigma_i^2) = \frac{1}{2n} \sum_i^k p(y_i) \sigma_i^2 \leq \frac{1}{2n} \sigma^2

Intuitively, the variance within a single grid cell $\sigma_i^2$ should be smaller than the variance of the plain MC estimator, because samples are closer together.

Here's a proof that $\sum_i^k  p(y_i)\sigma_i^2 \leq \sigma^2$:

Let $\mu_i = \mathbb{E}[X|Y=y_i], \sigma_i^2 = \Var[X|Y]$

\mathbb{E}[X^2] & = \mathbb{E}_Y[\mathbb{E}_X[X^2|Y]] && \text{Tower property} \\
& = \sum_i^k p(y_i) \mathbb{E}[X^2|Y=y_i] \\
& = \sum_i^k p(y_i) (\sigma_i^2 + \mu_i^2) &&  \text{via } \Var[X|Y] = \mathbb{E}[X^2|Y] - \mathbb{E}[X|Y=y_i]^2 \\
\mathbb{E}[X] & = \mathbb{E}_Y[\mathbb{E}_X[X|Y]] \\
& =\sum_i^k p(y_i) \mu_i \\
\sigma^2 & = \mathbb{E}[X^2] - \mathbb{E}[X]^2 \\
& =\sum_i^k p(y_i) \sigma_i^2 +  \big[\sum_i^k p(y_i) \mu_i^2 - (\sum_i^k p(y_i) \mu_i)^2\big]

Note that the term


corresponds to the variance of a categorical distribution that takes value $\mu_i$ with probability $p(y_i)$. This term is strictly non-negative, and therefore the inequality  $\sum_i^k  \frac{k}{2n}\sigma_i^2 \leq \sigma^2$ holds.

This yields an interesting observation: when we do stratified sampling, we are eliminating the variance between strata means. This implies that we get the most gains by choosing strata such that samples vary greatly between strata.

Here's a comparison of the empirical vs. theoretical variance with respect to number of strata:

# theoretical variance
strat_var = zeros(50)
for N=1:50
    sigma_i_2 = zeros(N)
    y = range(0,1/N,N)
    for i=1:N
        x1 = y[i].+1/N*rand(100)
        x2 = rand(100)
    n_i = 2
    strat_var[N] = sum((1/(N))^2*(1/n_i*sigma_i_2))    
s = estimatorVar(stratified)
plot(1:50,s,label="Empirical Variance",title="Stratified Sampling")
plot!(1:50,strat_var,label="Theoretical Variance")


  • Stratified Sampling can actually be combined with other variance reduction techniques - for instance, when computing the conditional estimate within a strata, you are free to use antithetic sampling, control variates within the strata. You can even do stratified sampling within your strata! This makes sense if your sampling domain is already partitioned into a bounding volume hierarchy, such as a KD-Tree.
  • Antithetic sampling can be regarded as a special case of stratified sampling with 2 strata. The inter-strata variance you are eliminating here is the self-symmetry in the distribution of $X$ (the covariance between one half and another half of the samples).


Here's a plot comparing the standard deviations of these estimators as a function of sample size. Keep in mind that your choice of variance reduction technique should depend on the problem, and no method is objectively better than the others. This is a very simple toy problem so all estimators perform pretty well.

I'm currently working on a follow-up tutorial on Importance Sampling. The explanation is a bit long, so I'm keeping it separate from this tutorial.

A toy example is all well and good, but where does variance reduction come up in modern scientific computing?

Scientific Data Analysis

At a very fundamental level, variance in scientific data either suggests an underlying noisy process or errors. Variance degrades the statistical significance of findings, so it's desirable to conduct experiments and measurements such that the variance of findings is low.

Quantitative Finance

In finance, we often simulate expected returns of portfolios by sampling over all possible movements in the market. Certain rare events (for example, a terrorist attack) are highly unlikely but have such a big impact that on the economy that we want to account for it in our expectation. Modeling these "black swan" events are of considerable interest to financial & actuarial industries.

Deep Learning

In minibatch stochastic gradient descent (the "hammer" of Deep Learning), we compute the parameter update using the expected gradient of the total loss across the training dataset. It takes too long to consider every point in the dataset, so we estimate gradients via stochastic minibatch. This is nothing more than a Monte Carlo estimate.

$$E_x[\nabla_\theta J(x;\theta)] = \int p(x) \nabla_\theta J(x;\theta) dx \approx \frac{1}{m}\sum_i^m \nabla_\theta J(x_i;\theta)$$

The less variance we have, the sooner our training converges.

Computer Graphics

In physically based rendering, we are trying to compute the total incoming light arriving at each camera pixel. The vast majority of paths contribute zero radiance, thus making our samples noisy. Decreasing variance allows us to "de-speckle" the pictures.

\mathcal{L}(x,\omega_o) & = \mathcal{L}_e(x,\omega_o) + \int_\Omega{f_r(x,\omega_i,\omega_o)\mathcal{L}(x^\prime,\omega_i)(\omega_o \cdot n_x) d\omega_i} \\
& \approx \mathcal{L}_e(x,\omega_o) + \frac{1}{m}\sum_i^m{f_r(x,\omega_i,\omega_o)\mathcal{L}(x^\prime,\omega_i)(\omega_o \cdot n_x)}

Sunday, August 7, 2016

A Beginner's Guide to Variational Methods: Mean-Field Approximation




Table of Contents

  1. Preliminaries and Notation
  2. Problem formulation
  3. Variational Lower Bound for Mean-field Approximation
  4. Forward KL vs. Reverse KL
  5. Connections to Deep Learning

Preliminaries and Notation

  • Uppercase $X$ denotes a random variable






$X = $ 

$X= $

$X = $ 



The various pieces of this are associated with common names:




Hidden Variables as Priors









Problem Formulation




Variational Lower Bound for Mean-field Approximation









\end{align} \\

Minimizing this is equivalent to maximizing the negation of this function:












Forward KL vs. Reverse KL







Minimizing the Reverse-KL has exactly the opposite behavior:





Connections to Deep Learning


Thanks for reading, and stay tuned!

Sunday, July 24, 2016

Why Randomness is Important for Deep Learning



I'm a big fan of Deep Learning




Random noise actually is crucial for getting DNNs to work well:


Single Input, Multiple Output











Randomness and Information Theory


To be honest, I still don't fully understand. 






Limiting Information in Neural Nets

What does this definition of randomness have to deal with randomness?










Exploration "Energy"










Closing Thoughts




Saturday, July 16, 2016

What product breakthroughs will recent advances in deep learning enable?


  • Large neural networks (millions of free parameters)
  • High performance computing ( thousands of processors running in parallel)

  • customized data compression
  • compressive sensing
  • data-driven sensor calibration
  • offline AI
  • human-computer interaction
  • gaming, artistic assistants
  • unstructured data mining
  • voice synthesis

Customized data compression


  • What if their hair is covering one of their eyes? 




Compressive sensing


  • more accurate reconstructions from sonar / LIDAR data

Data-driven sensor calibration







Offline AI




Some other possibilities:

Human-computer interaction



Dulight--Eyes for visually impaired












Artistic Assistants



Data Mining from Unstructured Data


For instance:
  • Parsing events from scanned flyers
  • identifying which products on EBay are the same
  • determining consumer sentiment from webcam
  • extracting blog content from pages without RSS feeds

Voice synthesis


Bonus: more products
