500 citations · 1.4k across the 23 of their papers we have counts for
4 papers · 1 filter
Efficiently Modeling Long Sequences with Structured State Spaces
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 depende…
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers
Albert Gu, Isys Johnson, Karan Goel +4
Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with uniqu…
HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections
Ines Chami, Albert Gu, Dat Nguyen +1
This paper studies Principal Component Analysis (PCA) for data lying in hyperbolic spaces. Given directions, PCA relies on: (1) a parameterization of subspaces spanned by these dir…
Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps
Tri Dao, Nimit S. Sohoni, Albert Gu +5
Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference…