61 citations · 72 across the 7 of their papers we have counts for
6 papers · 1 filter
HyBRA-EEG: Hyperbolic Global-Local Brain-Region Aggregation for Self-Supervised EEG Representation Learning
Xin Liu, Feng Lu, Wei Li
Electroencephalography (EEG) representation learning is challenged by noisy signals, inter-subject variability, and heterogeneous electrode montages in different datasets and recor…
On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networks
Xin Liu, Wei li, Dazhi Zhan +4
Federated learning (FL) is a widely employed distributed paradigm for collaboratively training machine learning models from multiple clients without sharing local data. In practice…
Provable Acceleration of Nesterov's Accelerated Gradient Method over Heavy Ball Method in Training Over-Parameterized Neural Networks
Xin Liu, Wei Tao, Wei Li +3
Due to its simplicity and efficiency, the first-order gradient method has been extensively employed in training neural networks. Although the optimization problem of the neural net…
FedSSO: A Federated Server-Side Second-Order Optimization Algorithm
Xin Ma, Renyi Bao, Jinpeng Jiang +5
In this work, we propose FedSSO, a server-side second-order optimization method for federated learning (FL). In contrast to previous works in this direction, we employ a server-sid…
A Convergence Analysis of Nesterov's Accelerated Gradient Method in Training Deep Linear Neural Networks
Xin Liu, Wei Tao, Zhisong Pan
Momentum methods, including heavy-ball~(HB) and Nesterov's accelerated gradient~(NAG), are widely used in training neural networks for their fast convergence. However, there is a l…
Provable Convergence of Nesterov's Accelerated Gradient Method for Over-Parameterized Neural Networks
Xin Liu, Zhisong Pan, Wei Tao
Momentum methods, such as heavy ball method~(HB) and Nesterov's accelerated gradient method~(NAG), have been widely used in training neural networks by incorporating the history of…