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Found 17 papers, 13 papers with code
Simple Hardware-Efficient Long Convolutions for Sequence Modeling
We find that a key requirement to achieving high performance is keeping the convolution kernels smooth.
Hungry Hungry Hippos: Towards Language Modeling with State Space Models
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)
How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections
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
Arithmetic Circuits, Structured Matrices and (not so) Deep Learning
This survey presents a necessarily incomplete (and biased) overview of results at the intersection of arithmetic circuit complexity, structured matrices and deep learning.
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
We also extend FlashAttention to block-sparse attention, yielding an approximate attention algorithm that is faster than any existing approximate attention method.
Monarch: Expressive Structured Matrices for Efficient and Accurate Training
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).
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models
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.
Scatterbrain: Unifying Sparse and Low-rank Attention Approximation
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.
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers
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
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers
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.
Scatterbrain: Unifying Sparse and Low-rank Attention
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.
Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps
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.
HiPPO: Recurrent Memory with Optimal Polynomial Projections
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
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations
Fast linear transforms are ubiquitous in machine learning, including the discrete Fourier transform, discrete cosine transform, and other structured transformations such as convolutions.
Learning Compressed Transforms with Low Displacement Rank
The low displacement rank (LDR) framework for structured matrices represents a matrix through two displacement operators and a low-rank residual.
Hypertree Decompositions Revisited for PGMs
We revisit the classical problem of exact inference on probabilistic graphical models (PGMs).
Hypertree Decompositions Revisited for PGMs
We revisit the classical problem of exact inference on probabilistic graphical models (PGMs).
