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20212024
most citedTowards Understanding the Generalizability of Delayed Stochastic Gradient Descent

2 citations · 5 across the 8 of their papers we have counts for

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cs.LG2024

Sharpness-Aware Minimization with Adaptive Regularization for Training Deep Neural Networks

Jinping Zou, Xiaoge Deng, Tao Sun

Sharpness-Aware Minimization (SAM) has proven highly effective in improving model generalization in machine learning tasks. However, SAM employs a fixed hyperparameter associated w…

cs.LG2024

Federated Prediction-Powered Inference from Decentralized Data

Ping Luo, Xiaoge Deng, Ziqing Wen +2

In various domains, the increasing application of machine learning allows researchers to access inexpensive predictive data, which can be utilized as auxiliary data for statistical…

cs.LG2024

Adaptive Momentum Benefits Score-based Generative Models

Ziqing Wen, Xiaoge Deng, Ping Luo +3

Score-based Generative Models (SGMs) have achieved remarkable success in generation tasks by establishing a diffusion process that gradually perturbs real data into Gaussian noise…

cs.LG2024

Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients

Ping Luo, Xiaoge Deng, Ziqing Wen +2

Federated Learning (FL) is a distributed machine learning framework in communication network systems. However, the systems' Non-Independent and Identically Distributed (Non-IID) da…

cs.LG2023★ 2 cited

Towards Understanding the Generalizability of Delayed Stochastic Gradient Descent

Xiaoge Deng, Li Shen, Shengwei Li +3

Stochastic gradient descent (SGD) performed in an asynchronous manner plays a crucial role in training large-scale machine learning models. However, the generalization performance…