125 citations · 439 across the 35 of their papers we have counts for
17 papers · 1 filter
ECC: Platform-Independent Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model
Haichuan Yang, Yuhao Zhu, Ji Liu
Many DNN-enabled vision applications constantly operate under severe energy constraints such as unmanned aerial vehicles, Augmented Reality headsets, and smartphones. Designing DNN…
Stochastic Primal-Dual Method for Empirical Risk Minimization with Per-Iteration Complexity
Conghui Tan, Tong Zhang, Shiqian Ma +1
Regularized empirical risk minimization problem with linear predictor appears frequently in machine learning. In this paper, we propose a new stochastic primal-dual method to solve…
Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application to Reinforcement Learning
Bo Liu, Luwan Zhang, Ji Liu
Dantzig Selector (DS) is widely used in compressed sensing and sparse learning for feature selection and sparse signal recovery. Since the DS formulation is essentially a linear pr…
Revisit Batch Normalization: New Understanding from an Optimization View and a Refinement via Composition Optimization
Xiangru Lian, Ji Liu
Batch Normalization (BN) has been used extensively in deep learning to achieve faster training process and better resulting models. However, whether BN works strongly depends on ho…
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space
Jiechao Xiong, Qing Wang, Zhuoran Yang +7
Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely. Motivated by applications in computer games, we…
Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search
Anji Liu, Jianshu Chen, Mingze Yu +3
Monte Carlo Tree Search (MCTS) algorithms have achieved great success on many challenging benchmarks (e.g., Computer Go). However, they generally require a large number of rollouts…