ml:state-space_models

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Table of Contents

State-Space Models
- Overviews
- Key Papers
- Papers
- People
- Related Pages

State-Space Models

Overviews

Key Papers

Gu et al 2020 - HiPPO: Recurrent Memory with Optimal Polynomial Projections
S4 model: Gu et al 2021 - Efficiently Modeling Long Sequences with Structured State Spaces
Gu et al 2022 - How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections
S5 model: Smith et al 2022 - Simplified State Space Layers for Sequence Modeling
Mega: Ma et al 2022 - Mega: Moving Average Equipped Gated Attention SOTA on long-range arena benchmark. Combines flash attention with state-space models. Still n-squared runtime however
H3 model: Fu et al 2022 - Hungry Hungry Hippos: Towards Language Modeling with State Space Models
Orvieto et al 2023 - Resurrecting Recurrent Neural Networks for Long Sequences Gives a nice history
Poli et al 2023 - Hyena Hierarchy: Towards Larger Convolutional Language Models
Sun et al 2023 - Retentive Network: A Successor to Transformer for Large Language Models
Gu & Dao 2023 - Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Papers

Vardasbi et al 2023 - State Spaces Aren’t Enough: Machine Translation Needs Attention

People

Albert Gu

Related Pages

RNN
Seq2seq

ml/state-space_models.1702435792.txt.gz · Last modified: 2023/12/13 02:49 by jmflanig