16 citations · 39 across the 8 of their papers we have counts for
8 papers
Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity Constraints
Hanchi Huang, Li Shen, Deheng Ye +1
We propose a novel master-slave architecture to solve the top- combinatorial multi-armed bandits problem with non-linear bandit feedback and diversity constraints, which, to the…
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Chao Xue, Wei Liu, Shuai Xie +25
Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to t…
Curriculum-based Asymmetric Multi-task Reinforcement Learning
Hanchi Huang, Deheng Ye, Li Shen +1
We introduce CAMRL, the first curriculum-based asymmetric multi-task learning (AMTL) algorithm for dealing with multiple reinforcement learning (RL) tasks altogether. To mitigate t…
Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution
Han Huang, Li Shen, Chaoyang He +2
Single Image Super-Resolution (SISR) tasks have achieved significant performance with deep neural networks. However, the large number of parameters in CNN-based met-hods for SISR t…
Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration
Congliang Chen, Li Shen, Fangyu Zou +1
Adam is one of the most influential adaptive stochastic algorithms for training deep neural networks, which has been pointed out to be divergent even in the simple convex setting v…
CPOT: Channel Pruning via Optimal Transport
Yucong Shen, Li Shen, Hao-Zhi Huang +2
Recent advances in deep neural networks (DNNs) lead to tremendously growing network parameters, making the deployments of DNNs on platforms with limited resources extremely difficu…
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
Zhishuai Guo, Mingrui Liu, Zhuoning Yuan +3
In this paper, we study distributed algorithms for large-scale AUC maximization with a deep neural network as a predictive model. Although distributed learning techniques have been…
Quantized Adam with Error Feedback
Congliang Chen, Li Shen, Haozhi Huang +1
In this paper, we present a distributed variant of adaptive stochastic gradient method for training deep neural networks in the parameter-server model. To reduce the communication…