Summary of Fedfa: a Fully Asynchronous Training Paradigm For Federated Learning, by Haotian Xu et al.
FedFa: A Fully Asynchronous Training Paradigm for Federated Learningby Haotian Xu, Zhaorui Zhang, Sheng Di,…
FedFa: A Fully Asynchronous Training Paradigm for Federated Learningby Haotian Xu, Zhaorui Zhang, Sheng Di,…
On the Empirical Complexity of Reasoning and Planning in LLMsby Liwei Kang, Zirui Zhao, David…
LMEraser: Large Model Unlearning through Adaptive Prompt Tuningby Jie Xu, Zihan Wu, Cong Wang, Xiaohua…
How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Modelby Umberto Tomasini,…
Insight Gained from Migrating a Machine Learning Model to Intelligence Processing Unitsby Hieu Le, Zhenhua…
Analytical Approximation of the ELBO Gradient in the Context of the Clutter Problemby Roumen Nikolaev…
Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregationby Mattia Litrico, Davide Talon, Sebastiano…
Do Counterfactual Examples Complicate Adversarial Training?by Eric Yeats, Cameron Darwin, Eduardo Ortega, Frank Liu, Hai…
HLAT: High-quality Large Language Model Pre-trained on AWS Trainiumby Haozheng Fan, Hao Zhou, Guangtai Huang,…
Asset management, condition monitoring and Digital Twins: damage detection and virtual inspection on a reinforced…