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2 code implementations • 31 Oct 2021 • Albert Gu, Karan Goel, Christopher Ré

A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range dependencies.

Ranked #1 on Long-range modeling on LRA

1 code implementation • 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.

1 code implementation • 7 Jun 2021 • Ines Chami, Albert Gu, Dat Nguyen, Christopher Ré

Given directions, PCA relies on: (1) a parameterization of subspaces spanned by these directions, (2) a method of projection onto subspaces that preserves information in these directions, and (3) an objective to optimize, namely the variance explained by projections.

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.

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.

1 code implementation • NeurIPS 2020 • Nimit S. Sohoni, Jared A. Dunnmon, Geoffrey Angus, Albert Gu, Christopher Ré

As the subclass labels are frequently unavailable, models trained using only the coarser-grained class labels often exhibit highly variable performance across different subclasses.

2 code implementations • NeurIPS 2020 • Ines Chami, Albert Gu, Vaggos Chatziafratis, Christopher Ré

Recently, Dasgupta reframed HC as a discrete optimization problem by introducing a global cost function measuring the quality of a given tree.

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 #6 on Sequential Image Classification on Sequential MNIST

1 code implementation • ICLR 2021 • Karan Goel, Albert Gu, Yixuan Li, Christopher Ré

Particularly concerning are models with inconsistent performance on specific subgroups of a class, e. g., exhibiting disparities in skin cancer classification in the presence or absence of a spurious bandage.

1 code implementation • ICML 2020 • Albert Gu, Caglar Gulcehre, Tom Le Paine, Matt Hoffman, Razvan Pascanu

Gating mechanisms are widely used in neural network models, where they allow gradients to backpropagate more easily through depth or time.

no code implementations • ICLR 2019 • Albert Gu, Frederic Sala, Beliz Gunel, Christopher Ré

The quality of the representations achieved by embeddings is determined by how well the geometry of the embedding space matches the structure of the data.

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.

1 code implementation • ICML 2018 • Christopher De Sa, Albert Gu, Christopher Ré, Frederic Sala

Given a tree, we give a combinatorial construction that embeds the tree in hyperbolic space with arbitrarily low distortion without using optimization.

no code implementations • 16 Mar 2018 • Tri Dao, Albert Gu, Alexander J. Ratner, Virginia Smith, Christopher De Sa, Christopher Ré

Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines.

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