Taming Transformer Without Using Learning Rate Warmup | Paper Citation Record

Observation 257220fa-7306-4f3c-b984-ee6914197076 · outbound

This paper cites Rezero is all you need: Fast convergence at large depth.

Taming Transformer Without Using Learning Rate Warmup Rezero is all you need: Fast convergence at large depth

Reference 1

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Observation 627bff35-0fc5-4536-92e4-04b298512953 · outbound

This paper cites Language models are few-shot learners.

Taming Transformer Without Using Learning Rate Warmup Language models are few-shot learners

Reference 2

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Observation 17532fd4-7a2f-4153-97f8-e259ec0ba405 · outbound

This paper cites Palm: Scaling language modeling with pathways.

Taming Transformer Without Using Learning Rate Warmup Palm: Scaling language modeling with pathways

Reference 3

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This paper cites The Road Less Scheduled.

Taming Transformer Without Using Learning Rate Warmup The Road Less Scheduled

Reference 4

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This paper cites Scaling vision transformers to 22 billion parameters.

Taming Transformer Without Using Learning Rate Warmup Scaling vision transformers to 22 billion parameters

Reference 5

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This paper cites Imagenet: A large-scale hierarchical image database.

Taming Transformer Without Using Learning Rate Warmup Imagenet: A large-scale hierarchical image database

Reference 6

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This paper cites Attention is not all you need: Pure attention loses rank doubly exponentially with depth.

Taming Transformer Without Using Learning Rate Warmup Attention is not all you need: Pure attention loses rank doubly exponentially with depth

Reference 7

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Taming Transformer Without Using Learning Rate Warmup An image is worth 16x16 words: Transformers for image recognition at scale

Reference 8

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This paper cites The Llama 3 Herd of Models.

Taming Transformer Without Using Learning Rate Warmup The Llama 3 Herd of Models

Reference 9

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This paper cites Adaptive subgradient methods for online learning and stochastic optimization.

Taming Transformer Without Using Learning Rate Warmup Adaptive subgradient methods for online learning and stochastic optimization

Reference 10

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This paper cites Openwebtext corpus.

Taming Transformer Without Using Learning Rate Warmup Openwebtext corpus

Reference 11

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This paper cites Matrix computations.

Taming Transformer Without Using Learning Rate Warmup Matrix computations

Reference 12

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This paper cites Kronecker products and matrix calculus with applications.

Taming Transformer Without Using Learning Rate Warmup Kronecker products and matrix calculus with applications

Reference 13

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This paper cites Flatten transformer: Vision transformer using focused linear attention.

Taming Transformer Without Using Learning Rate Warmup Flatten transformer: Vision transformer using focused linear attention

Reference 14

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This paper cites Query-key normalization for transformers.

Taming Transformer Without Using Learning Rate Warmup Query-key normalization for transformers

Reference 15

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This paper cites Topics in matrix analysis, 1991.

Taming Transformer Without Using Learning Rate Warmup Topics in matrix analysis, 1991

Reference 16

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This paper cites Matrix analysis.

Taming Transformer Without Using Learning Rate Warmup Matrix analysis

Reference 17

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Taming Transformer Without Using Learning Rate Warmup Unresolved cited work

Reference 18

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This paper cites The lipschitz constant of self-attention.

Taming Transformer Without Using Learning Rate Warmup The lipschitz constant of self-attention

Reference 19

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Observation 4d968143-41b1-4785-b4cf-c4f63278e786 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Taming Transformer Without Using Learning Rate Warmup Adam: A Method for Stochastic Optimization

Reference 20

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This paper cites Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks.

Taming Transformer Without Using Learning Rate Warmup Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks

Reference 21

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This paper cites Analyzing & reducing the need for learning rate warmup in gpt training.

Taming Transformer Without Using Learning Rate Warmup Analyzing & reducing the need for learning rate warmup in gpt training

Reference 22

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This paper cites Backpropagation applied to handwritten zip code recognition.

Taming Transformer Without Using Learning Rate Warmup Backpropagation applied to handwritten zip code recognition

Reference 23

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This paper cites Gradient-based learning applied to document recognition.

Taming Transformer Without Using Learning Rate Warmup Gradient-based learning applied to document recognition

Reference 24

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This paper cites Efficient backprop.

Taming Transformer Without Using Learning Rate Warmup Efficient backprop

Reference 25

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This paper cites Understanding the difficulty of training transformers.

Taming Transformer Without Using Learning Rate Warmup Understanding the difficulty of training transformers

Reference 26

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Taming Transformer Without Using Learning Rate Warmup Swin transformer: Hierarchical vision transformer using shifted windows

Reference 27

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This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Taming Transformer Without Using Learning Rate Warmup SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 28

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This paper cites Fixing weight decay regularization in adam.

Taming Transformer Without Using Learning Rate Warmup Fixing weight decay regularization in adam

Reference 29

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This paper cites Signal propagation in transformers: Theoretical perspectives and the role of rank collapse.

Taming Transformer Without Using Learning Rate Warmup Signal propagation in transformers: Theoretical perspectives and the role of rank collapse

Reference 30

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This paper cites Scalable diffusion models with transformers.

Taming Transformer Without Using Learning Rate Warmup Scalable diffusion models with transformers

Reference 31

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This paper cites The matrix cookbook.

Taming Transformer Without Using Learning Rate Warmup The matrix cookbook

Reference 32

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This paper cites Lipsformer: Introducing lipschitz continuity to vision transformers.

Taming Transformer Without Using Learning Rate Warmup Lipsformer: Introducing lipschitz continuity to vision transformers

Reference 33

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This paper cites Understanding Optimization of Deep Learning via Jacobian Matrix and Lipschitz Constant.

Taming Transformer Without Using Learning Rate Warmup Understanding Optimization of Deep Learning via Jacobian Matrix and Lipschitz Constant

Reference 34

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This paper cites Improving language understanding by generative pre-training.

Taming Transformer Without Using Learning Rate Warmup Improving language understanding by generative pre-training

Reference 35

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This paper cites Language models are unsupervised multitask learners.

Taming Transformer Without Using Learning Rate Warmup Language models are unsupervised multitask learners

Reference 36

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This paper cites Learning transferable visual models from natural language supervision.

Taming Transformer Without Using Learning Rate Warmup Learning transferable visual models from natural language supervision

Reference 37

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This paper cites Zero-shot text-to-image generation.

Taming Transformer Without Using Learning Rate Warmup Zero-shot text-to-image generation

Reference 38

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This paper cites A stochastic approximation method.

Taming Transformer Without Using Learning Rate Warmup A stochastic approximation method

Reference 39

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This paper cites Learning representations by back-propagating errors.

Taming Transformer Without Using Learning Rate Warmup Learning representations by back-propagating errors

Reference 40

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This paper cites Cyclical learning rates for training neural networks.

Taming Transformer Without Using Learning Rate Warmup Cyclical learning rates for training neural networks

Reference 41

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This paper cites Scan and snap: Understanding training dynamics and token composition in 1-layer transformer.

Taming Transformer Without Using Learning Rate Warmup Scan and snap: Understanding training dynamics and token composition in 1-layer transformer

Reference 42

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This paper cites JoMA: Demystifying Multilayer Transformers via JOint Dynamics of MLP and Attention.

Taming Transformer Without Using Learning Rate Warmup JoMA: Demystifying Multilayer Transformers via JOint Dynamics of MLP and Attention

Reference 43

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This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Taming Transformer Without Using Learning Rate Warmup Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 44

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This paper cites Attention is all you need.

Taming Transformer Without Using Learning Rate Warmup Attention is all you need

Reference 45

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This paper cites High-dimensional probability: An introduction with applications in data science, volume 47.

Taming Transformer Without Using Learning Rate Warmup High-dimensional probability: An introduction with applications in data science, volume 47

Reference 46

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This paper cites DeepNet: Scaling Transformers to 1,000 Layers.

Taming Transformer Without Using Learning Rate Warmup DeepNet: Scaling Transformers to 1,000 Layers

Reference 47

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This paper cites Learning Deep Transformer Models for Machine Translation.

Taming Transformer Without Using Learning Rate Warmup Learning Deep Transformer Models for Machine Translation

Reference 48

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This paper cites Pytorch image models.

Taming Transformer Without Using Learning Rate Warmup Pytorch image models

Reference 49

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This paper cites High-dimensional data analysis with low-dimensional models: Principles, computation, and applications.

Taming Transformer Without Using Learning Rate Warmup High-dimensional data analysis with low-dimensional models: Principles, computation, and applications

Reference 50

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This paper cites On layer normalization in the transformer architecture.

Taming Transformer Without Using Learning Rate Warmup On layer normalization in the transformer architecture

Reference 51

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This paper cites Stabilizing transformer training by preventing attention entropy collapse.

Taming Transformer Without Using Learning Rate Warmup Stabilizing transformer training by preventing attention entropy collapse

Reference 52

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This paper cites Root mean square layer normalization.

Taming Transformer Without Using Learning Rate Warmup Root mean square layer normalization

Reference 53

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This paper cites write newline.

Taming Transformer Without Using Learning Rate Warmup write newline

Reference 54

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This paper cites @esa (Ref.

Taming Transformer Without Using Learning Rate Warmup @esa (Ref

Reference 55

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Taming Transformer Without Using Learning Rate Warmup Unresolved cited work

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Taming Transformer Without Using Learning Rate Warmup Unresolved cited work

Reference 57

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