Customizing the Inductive Biases of Softmax Attention using Structured Matrices | Paper Citation Record

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This paper cites write newline.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices write newline

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This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 2

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This paper cites On the Benefits of Rank in Attention Layers.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices On the Benefits of Rank in Attention Layers

Reference 3

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This paper cites Chronos: Learning the Language of Time Series.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Chronos: Learning the Language of Time Series

Reference 4

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This paper cites Simple linear attention language models balance the recall-throughput tradeoff.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Simple linear attention language models balance the recall-throughput tradeoff

Reference 5

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This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Neural Machine Translation by Jointly Learning to Align and Translate

Reference 6

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices Titans: Learning to Memorize at Test Time

Reference 8

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This paper cites Longformer: The Long-Document Transformer.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Longformer: The Long-Document Transformer

Reference 9

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This paper cites S., Reddi, S., and Kumar, S.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices S., Reddi, S., and Kumar, S

Reference 10

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This paper cites On the Opportunities and Risks of Foundation Models.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices On the Opportunities and Risks of Foundation Models

Reference 11

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This paper cites Scatterbrain: Unifying sparse and low-rank attention.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Scatterbrain: Unifying sparse and low-rank attention

Reference 12

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This paper cites Generating Long Sequences with Sparse Transformers.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Generating Long Sequences with Sparse Transformers

Reference 13

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This paper cites Rethinking Attention with Performers.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Rethinking Attention with Performers

Reference 14

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This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 15

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This paper cites Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations

Reference 16

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This paper cites Monarch: Expressive Structured Matrices for Efficient and Accurate Training.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Monarch: Expressive Structured Matrices for Efficient and Accurate Training

Reference 17

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This paper cites Hungry Hungry Hippos: Towards Language Modeling with State Space Models.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Hungry Hungry Hippos: Towards Language Modeling with State Space Models

Reference 18

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This paper cites What Can Transformers Learn In-Context? A Case Study of Simple Function Classes.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices What Can Transformers Learn In-Context? A Case Study of Simple Function Classes

Reference 19

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This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 20

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This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Efficiently Modeling Long Sequences with Structured State Spaces

Reference 21

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This paper cites SLT rain: a sparse plus low rank approach for parameter and memory efficient pretraining.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices SLT rain: a sparse plus low rank approach for parameter and memory efficient pretraining

Reference 22

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This paper cites Global context vision transformers.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Global context vision transformers

Reference 23

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices LoRA: Low-Rank Adaptation of Large Language Models

Reference 24

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This paper cites Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey

Reference 25

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This paper cites Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers

Reference 26

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This paper cites M., and Malach, E.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices M., and Malach, E

Reference 27

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This paper cites Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 28

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices Towards Understanding Inductive Bias in Transformers: A View From Infinity

Reference 29

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This paper cites Tune: A Research Platform for Distributed Model Selection and Training.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Tune: A Research Platform for Distributed Model Selection and Training

Reference 30

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This paper cites Decoupled Weight Decay Regularization.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Decoupled Weight Decay Regularization

Reference 31

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices Non-Vacuous Generalization Bounds for Large Language Models

Reference 32

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models

Reference 33

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices Unresolved cited work

Reference 34

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This paper cites G., Challú, C., Garza, A., Canseco, M.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices G., Challú, C., Garza, A., Canseco, M

Reference 35

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices Factor fitting, rank allocation, and partitioning in multilevel low rank matrices, 2023

Reference 36

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices Fitting Multilevel Factor Models

Reference 37

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This paper cites Hyena Hierarchy: Towards Larger Convolutional Language Models.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Hyena Hierarchy: Towards Larger Convolutional Language Models

Reference 38

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices

Reference 39

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices Compute Better Spent: Replacing Dense Layers with Structured Matrices

Reference 40

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices Combiner: full attention transformer with sparse computation cost

Reference 41

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices Gemma 2: Improving Open Language Models at a Practical Size

Reference 42

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This paper cites Representational strengths and limitations of transformers.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Representational strengths and limitations of transformers

Reference 43

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices A., Choromanski, K

Reference 44

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This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 45

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices T., Gu, A., Dao, T., Rudra, A., and R\' e , C

Reference 46

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices N., Kaiser, L

Reference 47

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This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 48

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices Building on efficient foundations: Effective training of LLM s with structured feedforward layers

Reference 49

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices MSWA: Refining Local Attention with Multi-ScaleWindow Attention

Reference 50

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This paper cites Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Reference 51

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices A Spectral Condition for Feature Learning

Reference 52

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Customizing the Inductive Biases of Softmax Attention using Structured Matrices BP-Transformer: Modelling Long-Range Context via Binary Partitioning

Reference 53

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This paper cites Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

Reference 54

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This paper cites The hedgehog & the porcupine: Expressive linear attentions with softmax mimicry.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices The hedgehog & the porcupine: Expressive linear attentions with softmax mimicry

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This paper cites Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Reference 56

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This paper cites and Soricut, R.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices and Soricut, R

Reference 57

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