Summary From Deep to Long Learning? · Hazy Research hazyresearch.stanford.edu
1,654 words - html page - View html page
One Line
The article discusses the challenge of modeling long-range dependencies in machine learning and the introduction of the Long Range Arena benchmark, as well as various models such as Hippo, S4, H3, and Hyena, with Hazy Research exploring longer-sequence models for applications such as high-resolution imaging and language models that can read entire books.
Key Points
- Hazy Research is exploring longer-sequence models for deep learning, motivated by applications such as high-resolution imaging and language models.
- They are proposing new architectures, including the H3 and Hyena models, which use gating behavior and convolutions to handle longer sequences.
- Other models that can handle longer sequences include Hippo, S4, and FlashAttention.
- The Long Range Arena benchmark evaluates models' ability to handle long-range dependencies, and researchers are investigating nearly linear time models to address this issue.
- Longer sequences could enable machine learning models to learn from longer contexts, multiple media sources, and complex demonstrations.
- Hazy Research has proposed the first fully near linear-time convolutional models that can match Transformers on perplexity and downstream tasks with promising results in initial scaling experiments.
Summaries
267 word summary
The article discusses the challenge of modeling long-range dependencies in machine learning and the need for models that can handle longer sequences. The Long Range Arena (LRA) benchmark was introduced in 2020 to evaluate different models' ability to handle long-range dependencies. Researchers have been investigating models that are nearly linear time in sequence length, such as Hippo, S4, H3, and Hyena, to address this issue. Another approach to increasing sequence length is the use of images as context. The article also mentions the introduction of FlashAttention by the Hazy Research lab. The H3 model uses gating behavior and iteratively applies convolutions and gates. The Hyena model adds more projections and gates to generalize to more expressive architectures and close the gap to attention. The convolution can be computed in FlexConv or CKConv, and every SSM can be viewed as a convolution filter the length of the input sequence. The Hyena model grows nearly linearly in sequence length, and the next architecture in this line of work is RWKV. SSMs with gating can work well in concert with attention in language modeling. The FlashAttention model increases the sequence length of Transformers. The S4 model successfully models long-range dependencies and scales with sequence length. Hazy Research is exploring longer-sequence models for applications such as high-resolution imaging and language models that can read entire books. They are also looking at ways to make the connection between the FFT and matrix multiplication more efficient. Hazy Research has proposed the first fully near linear-time convolutional models that can match Transformers on perplexity and downstream tasks with promising results in initial scaling experiments.
369 word summary
Hazy Research is excited about exploring longer and longer sequences and new architectures for deep learning. They are motivated by applications that could benefit from longer-sequence models, such as high-resolution imaging and language models that can read entire books. They are exploring the class of transforms this extension learns and what it can allow them to do. They are also looking at ways to make the connection between the FFT and matrix multiplication more efficient. Hazy Research has proposed the first fully near linear-time convolutional models that can match Transformers on perplexity and downstream tasks with promising results in initial scaling experiments. The H3 model uses gating behavior that takes three projections of the input and iteratively applies convolutions and gates. The Hyena model adds more projections and gates to generalize to more expressive architectures and close the gap to attention. The convolution can be computed in FlexConv or CKConv, and every SSM can be viewed as a convolution filter the length of the input sequence. The Hyena model grows nearly linearly in sequence length, and the next architecture in this line of work is RWKV. SSMs with gating can work well in concert with attention in language modeling. The Hungry Hungry Hippos layer stacks two SSMs and multiplies their outputs together with a multiplicative gate. The FlashAttention model increases the sequence length of Transformers. The S4 model successfully models long-range dependencies and scales with sequence length. The article discusses the challenge of modeling long-range dependencies in machine learning and the need for models that can handle longer sequences. The Long Range Arena (LRA) benchmark was introduced in 2020 to evaluate different models' ability to handle long-range dependencies, but many Transformer variants struggled to perform better than random guessing. To address this issue, researchers have been investigating models that are nearly linear time in sequence length, such as Hippo, S4, H3, and Hyena. Another approach to increasing sequence length is the use of images as context. The context lengths of foundation models have been growing recently, and longer sequences could enable machine learning models to learn from longer contexts, multiple media sources, complex demonstrations, and more. The article also mentions the introduction of FlashAttention by the Hazy Research lab.