Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size | Paper Citation Record

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This paper cites Why Do We Need Weight Decay in Modern Deep Learning?.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Why Do We Need Weight Decay in Modern Deep Learning?

Reference 1

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This paper cites Complexity guarantees for polyak steps with momentum.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Complexity guarantees for polyak steps with momentum

Reference 2

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This paper cites signsgd: Compressed optimisation for non-convex problems.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size signsgd: Compressed optimisation for non-convex problems

Reference 3

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This paper cites GPT-NeoX-20B: An Open-Source Autoregressive Language Model.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size GPT-NeoX-20B: An Open-Source Autoregressive Language Model

Reference 4

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This paper cites On the fast convergence of minibatch heavy ball momentum.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size On the fast convergence of minibatch heavy ball momentum

Reference 5

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This paper cites Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit

Reference 6

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This paper cites Language Models are Few-Shot Learners.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Language Models are Few-Shot Learners

Reference 7

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Symbolic discovery of optimization algorithms

Reference 8

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This paper cites On Empirical Comparisons of Optimizers for Deep Learning.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size On Empirical Comparisons of Optimizers for Deep Learning

Reference 9

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This paper cites Palm: Scaling language modeling with pathways.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Palm: Scaling language modeling with pathways

Reference 10

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This paper cites Adaptive methods through the lens of SDE s: Theoretical insights on the role of noise.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Adaptive methods through the lens of SDE s: Theoretical insights on the role of noise

Reference 11

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Observation 6aec30bb-a9c7-48b2-aea7-063f9ac1c952 · outbound

This paper cites Momentum improves normalized sgd.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Momentum improves normalized sgd

Reference 12

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This paper cites Momentum-based variance reduction in non-convex sgd.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Momentum-based variance reduction in non-convex sgd

Reference 13

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

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Imagenet: A large-scale hierarchical image database

Reference 14

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

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size An image is worth 16x16 words: Transformers for image recognition at scale

Reference 15

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

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Adaptive subgradient methods for online learning and stochastic optimization

Reference 16

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This paper cites Momentum provably improves error feedback! Advances in Neural Information Processing Systems, 2024.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Momentum provably improves error feedback! Advances in Neural Information Processing Systems, 2024

Reference 17

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This paper cites Lecture 6: Matrix norms and spectral radii.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Lecture 6: Matrix norms and spectral radii

Reference 18

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This paper cites When and Why Momentum Accelerates SGD:An Empirical Study.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size When and Why Momentum Accelerates SGD:An Empirical Study

Reference 19

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This paper cites Handbook of Convergence Theorems for (Stochastic) Gradient Methods.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Handbook of Convergence Theorems for (Stochastic) Gradient Methods

Reference 20

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This paper cites Global convergence of the heavy-ball method for convex optimization.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Global convergence of the heavy-ball method for convex optimization

Reference 21

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This paper cites Goodfellow, Yoshua Bengio, and Aaron Courville.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Goodfellow, Yoshua Bengio, and Aaron Courville

Reference 22

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This paper cites Sgd for structured nonconvex functions: Learning rates, minibatching and interpolation.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Sgd for structured nonconvex functions: Learning rates, minibatching and interpolation

Reference 23

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This paper cites Analysis of an Idealized Stochastic Polyak Method and its Application to Black-Box Model Distillation.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Analysis of an Idealized Stochastic Polyak Method and its Application to Black-Box Model Distillation

Reference 24

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Deep residual learning for image recognition

Reference 25

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This paper cites Neural networks for machine learning lecture 6a overview of mini-batch gradient descent.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Neural networks for machine learning lecture 6a overview of mini-batch gradient descent

Reference 26

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This paper cites Empirical tests of optimization assumptions in deep learning.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Empirical tests of optimization assumptions in deep learning

Reference 27

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Long short-term memory

Reference 28

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Training Compute-Optimal Large Language Models

Reference 29

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Loss landscape characterization of neural networks without over-parametrization

Reference 30

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This paper cites Near optimal decentralized optimization with compression and momentum tracking.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Near optimal decentralized optimization with compression and momentum tracking

Reference 31

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Double momentum and error feedback for clipping with fast rates and differential privacy

Reference 32

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Accelerating stochastic gradient descent for least squares regression

Reference 33

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Towards understanding how momentum improves generalization in deep learning

Reference 34

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Adaptive sgd with polyak stepsize and line-search: Robust convergence and variance reduction

Reference 35

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Muon: An optimizer for hidden layers in neural networks, 2024

Reference 36

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Scaling Laws for Neural Language Models

Reference 37

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size char-rnn

Reference 38

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Unresolved cited work

Reference 39

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Adam: A method for stochastic optimization

Reference 40

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Learning multiple layers of features from tiny images

Reference 41

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Noise is not the main factor behind the gap between sgd and adam on transformers, but sign descent might be

Reference 42

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models

Reference 43

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Trajectory of mini-batch momentum: batch size saturation and convergence in high dimensions

Reference 44

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Convergence of adam under relaxed assumptions

Reference 45

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size PyTorch Distributed: Experiences on Accelerating Data Parallel Training

Reference 46

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size SP2: A Second Order Stochastic Polyak Method

Reference 47

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size An improved analysis of stochastic gradient descent with momentum

Reference 48

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Stochastic polyak step-size for sgd: An adaptive learning rate for fast convergence

Reference 49

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Decoupled Weight Decay Regularization

Reference 50

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Adaptive Gradient Methods with Dynamic Bound of Learning Rate

Reference 51

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Quasi-hyperbolic momentum and Adam for deep learning

Reference 52

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Pointer sentinel mixture models

Reference 53

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This paper cites Recurrent neural network based language model.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Recurrent neural network based language model

Reference 54

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This paper cites A Theory on Adam Instability in Large-Scale Machine Learning.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size A Theory on Adam Instability in Large-Scale Machine Learning

Reference 55

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Signal propagation in transformers: Theoretical perspectives and the role of rank collapse

Reference 56

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Observation 75b19c71-4040-4864-bd6d-b4cb27e0ecd2 · outbound

This paper cites Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance

Reference 57

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This paper cites The Cost of Training NLP Models: A Concise Overview.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size The Cost of Training NLP Models: A Concise Overview

Reference 58

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes

Reference 59

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Dynamics of sgd with stochastic polyak stepsizes: Truly adaptive variants and convergence to exact solution

Reference 60

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales

Reference 61

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Automatic differentiation in pytorch

Reference 62

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size The fineweb datasets: Decanting the web for the finest text data at scale

Reference 63

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Unresolved cited work

Reference 64

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Introduction to optimization

Reference 65

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Improving language understanding by generative pre-training

Reference 66

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Zero: Memory optimizations toward training trillion parameter models

Reference 67

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Local Curvature Descent: Squeezing More Curvature out of Standard and Polyak Gradient Descent

Reference 68

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size An adaptive polyak heavy-ball method

Reference 69

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Stochastic sign descent methods: New algorithms and better theory

Reference 70

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Unresolved cited work

Reference 71

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This paper cites The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training

Reference 72

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Almost sure convergence rates for stochastic gradient descent and stochastic heavy ball

Reference 73

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size GLU Variants Improve Transformer

Reference 74

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 75

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 76

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Reference 77

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size SlimPajama: A 627B token cleaned and deduplicated version of RedPajama , 2023

Reference 78

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Roformer: Enhanced transformer with rotary position embedding, 2023

Reference 79

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Training data-efficient image transformers & distillation through attention

Reference 80

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size LLaMA: Open and Efficient Foundation Language Models

Reference 81

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Closing the gap between the upper bound and lower bound of adam's iteration complexity

Reference 82

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This paper cites A modular analysis of provable acceleration via polyak’s momentum: Training a wide relu network and a deep linear network.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size A modular analysis of provable acceleration via polyak’s momentum: Training a wide relu network and a deep linear network

Reference 83

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Provable acceleration of heavy ball beyond quadratics for a class of polyak-lojasiewicz functions when the non-convexity is averaged-out

Reference 84

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Generalized polyak step size for first order optimization with momentum

Reference 85

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Adagrad stepsizes: Sharp convergence over nonconvex landscapes

Reference 86

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Pytorch image models

Reference 87

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size The marginal value of adaptive gradient methods in machine learning

Reference 88

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Small-scale proxies for large-scale Transformer training instabilities

Reference 89

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This paper cites Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling

Reference 90

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Reference 91

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size A Spectral Condition for Feature Learning

Reference 92

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Root mean square layer normalization, 2019

Reference 93

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Three Mechanisms of Weight Decay Regularization

Reference 94

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 95

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Why are adaptive methods good for attention models? Advances in Neural Information Processing Systems, 2020

Reference 96

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance

Reference 97

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This paper cites OPT: Open Pre-trained Transformer Language Models.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size OPT: Open Pre-trained Transformer Language Models

Reference 98

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Why Transformers Need Adam: A Hessian Perspective

Reference 99

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Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Adabelief optimizer: Adapting stepsizes by the belief in observed gradients

Reference 100

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