128 citations · 505 across the 21 of their papers we have counts for
6 papers · 1 filter
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Chia-Yu Chen, Jiamin Ni, Songtao Lu +8
Large-scale distributed training of Deep Neural Networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained. To overcome this limitation, num…
Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond
Tsung-Hui Chang, Mingyi Hong, Hoi-To Wai +2
Distributed learning has become a critical enabler of the massively connected world envisioned by many. This article discusses four key elements of scalable distributed processing…
Learn Electronic Health Records by Fully Decentralized Federated Learning
Songtao Lu, Yawen Zhang, Yunlong Wang +1
Federated learning opens a number of research opportunities due to its high communication efficiency in distributed training problems within a star network. In this paper, we focus…
No-regret Non-convex Online Meta-Learning
Zhenxun Zhuang, Yunlong Wang, Kezi Yu +1
The online meta-learning framework is designed for the continual lifelong learning setting. It bridges two fields: meta-learning which tries to extract prior knowledge from past ta…
Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML
Sijia Liu, Songtao Lu, Xiangyi Chen +5
In this paper, we study the problem of constrained robust (min-max) optimization ina black-box setting, where the desired optimizer cannot access the gradients of the objective fun…
Power Market Price Forecasting via Deep Learning
Yongli Zhu, Songtao Lu, Renchang Dai +2
A study on power market price forecasting by deep learning is presented. As one of the most successful deep learning frameworks, the LSTM (Long short-term memory) neural network is…