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Scaling test-time compute
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Enhance math problem solving by scaling test-time compute
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15 types of attention mechanisms
Attention mechanisms allow models to dynamically focus on specific parts of their input when performing tasks. In our recent article, we discussed Multi-Head Latent Attention (MLA) in detail and now it's time to summarize other existing types of attention.
Here is a list of 15 types of attention mechanisms used in AI models:
1. Soft attention (Deterministic attention) -> https://huggingface.co/papers/1409.0473
Assigns a continuous weight distribution over all parts of the input. It produces a weighted sum of the input using attention weights that sum to 1.
2. Hard attention (Stochastic attention) -> https://huggingface.co/papers/1508.04025
Makes a discrete selection of some part of the input to focus on at each step, rather than attending to everything.
3. Self-attention -> https://huggingface.co/papers/1706.03762
Each element in the sequence "looks" at other elements and "decides" how much to borrow from each of them for its new representation.
4. Cross-Attention (Encoder-Decoder attention) -> https://huggingface.co/papers/2104.08771
The queries come from one sequence and the keys/values come from another sequence. It allows a model to combine information from two different sources.
5. Multi-Head Attention (MHA) -> https://huggingface.co/papers/1706.03762
Multiple attention “heads” are run in parallel. The model computes several attention distributions (heads), each with its own set of learned projections of queries, keys, and values.
6. Multi-Head Latent Attention (MLA) -> https://huggingface.co/papers/2405.04434
Extends MHA by incorporating a latent space where attention heads can dynamically learn different latent factors or representations.
7. Memory-Based attention -> https://huggingface.co/papers/1503.08895
Involves an external memory and uses attention to read from and write to this memory.
See other types in the comments 👇
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15 types of attention mechanisms
Attention mechanisms allow models to dynamically focus on specific parts of their input when performing tasks. In our recent article, we discussed Multi-Head Latent Attention (MLA) in detail and now it's time to summarize other existing types of attention.
Here is a list of 15 types of attention mechanisms used in AI models:
1. Soft attention (Deterministic attention) -> https://huggingface.co/papers/1409.0473
Assigns a continuous weight distribution over all parts of the input. It produces a weighted sum of the input using attention weights that sum to 1.
2. Hard attention (Stochastic attention) -> https://huggingface.co/papers/1508.04025
Makes a discrete selection of some part of the input to focus on at each step, rather than attending to everything.
3. Self-attention -> https://huggingface.co/papers/1706.03762
Each element in the sequence "looks" at other elements and "decides" how much to borrow from each of them for its new representation.
4. Cross-Attention (Encoder-Decoder attention) -> https://huggingface.co/papers/2104.08771
The queries come from one sequence and the keys/values come from another sequence. It allows a model to combine information from two different sources.
5. Multi-Head Attention (MHA) -> https://huggingface.co/papers/1706.03762
Multiple attention “heads” are run in parallel. The model computes several attention distributions (heads), each with its own set of learned projections of queries, keys, and values.
6. Multi-Head Latent Attention (MLA) -> https://huggingface.co/papers/2405.04434
Extends MHA by incorporating a latent space where attention heads can dynamically learn different latent factors or representations.
7. Memory-Based attention -> https://huggingface.co/papers/1503.08895
Involves an external memory and uses attention to read from and write to this memory.
See other types in the comments 👇
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