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1 code implementation • 13 Feb 2023 • Daniel Y. Fu, Elliot L. Epstein, Eric Nguyen, Armin W. Thomas, Michael Zhang, Tri Dao, Atri Rudra, Christopher Ré

We find that a key requirement to achieving high performance is keeping the convolution kernels smooth.

3 code implementations • 28 Dec 2022 • Daniel Y. Fu, Tri Dao, Khaled K. Saab, Armin W. Thomas, Atri Rudra, Christopher Ré

First, we use synthetic language modeling tasks to understand the gap between SSMs and attention.

Ranked #2 on Language Modelling on WikiText-103 (using extra training data)

1 code implementation • 24 Jun 2022 • Albert Gu, Isys Johnson, Aman Timalsina, Atri Rudra, Christopher Ré

Linear time-invariant state space models (SSM) are a classical model from engineering and statistics, that have recently been shown to be very promising in machine learning through the Structured State Space sequence model (S4).

Ranked #7 on Long-range modeling on LRA

no code implementations • 24 Jun 2022 • Atri Rudra

This survey presents a necessarily incomplete (and biased) overview of results at the intersection of arithmetic circuit complexity, structured matrices and deep learning.

5 code implementations • 27 May 2022 • Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré

We also extend FlashAttention to block-sparse attention, yielding an approximate attention algorithm that is faster than any existing approximate attention method.

1 code implementation • 1 Apr 2022 • Tri Dao, Beidi Chen, Nimit Sohoni, Arjun Desai, Michael Poli, Jessica Grogan, Alexander Liu, Aniruddh Rao, Atri Rudra, Christopher Ré

To address these issues, we propose a class of matrices (Monarch) that is hardware-efficient (they are parameterized as products of two block-diagonal matrices for better hardware utilization) and expressive (they can represent many commonly used transforms).

1 code implementation • ICLR 2022 • Tri Dao, Beidi Chen, Kaizhao Liang, Jiaming Yang, Zhao Song, Atri Rudra, Christopher Ré

To address this, our main insight is to optimize over a continuous superset of sparse matrices with a fixed structure known as products of butterfly matrices.

1 code implementation • NeurIPS 2021 • Beidi Chen, Tri Dao, Eric Winsor, Zhao Song, Atri Rudra, Christopher Ré

Recent advances in efficient Transformers have exploited either the sparsity or low-rank properties of attention matrices to reduce the computational and memory bottlenecks of modeling long sequences.

2 code implementations • NeurIPS 2021 • Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, Christopher Ré

Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling power and computational efficiency.

Ranked #2 on Sequential Image Classification on Sequential MNIST

no code implementations • NeurIPS 2021 • Albert Gu, Isys Johnson, Karan Goel, Khaled Kamal Saab, Tri Dao, Atri Rudra, Christopher Re

Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling power and computational efficiency.

1 code implementation • NeurIPS 2021 • Beidi Chen, Tri Dao, Eric Winsor, Zhao Song, Atri Rudra, Christopher Ré

Recent advances in efficient Transformers have exploited either the sparsity or low-rank properties of attention matrices to reduce the computational and memory bottlenecks of modeling long sequences.

2 code implementations • ICLR 2020 • Tri Dao, Nimit S. Sohoni, Albert Gu, Matthew Eichhorn, Amit Blonder, Megan Leszczynski, Atri Rudra, Christopher Ré

Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference speed and reduce memory usage compared to general linear maps.

2 code implementations • NeurIPS 2020 • Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Re

A central problem in learning from sequential data is representing cumulative history in an incremental fashion as more data is processed.

Ranked #8 on Sequential Image Classification on Sequential MNIST

1 code implementation • 14 Mar 2019 • Tri Dao, Albert Gu, Matthew Eichhorn, Atri Rudra, Christopher Ré

Fast linear transforms are ubiquitous in machine learning, including the discrete Fourier transform, discrete cosine transform, and other structured transformations such as convolutions.

1 code implementation • NeurIPS 2018 • Anna T. Thomas, Albert Gu, Tri Dao, Atri Rudra, Christopher Ré

The low displacement rank (LDR) framework for structured matrices represents a matrix through two displacement operators and a low-rank residual.

no code implementations • 2 Jul 2018 • Aarthy Shivram Arun, Sai Vikneshwar Mani Jayaraman, Christopher Ré, Atri Rudra

We revisit the classical problem of exact inference on probabilistic graphical models (PGMs).

no code implementations • 5 Apr 2018 • Aarthy Shivram Arun, Sai Vikneshwar Mani Jayaraman, Christopher Ré, Atri Rudra

We revisit the classical problem of exact inference on probabilistic graphical models (PGMs).

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