8 citations · 9 across the 5 of their papers we have counts for
9 papers · 1 filter
Low-Rank+Sparse Tensor Compression for Neural Networks
Cole Hawkins, Haichuan Yang, Meng Li +2
Low-rank tensor compression has been proposed as a promising approach to reduce the memory and compute requirements of neural networks for their deployment on edge devices. Tensor…
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
Haotao Wang, Shupeng Gui, Haichuan Yang +2
Generative adversarial networks (GANs) have gained increasing popularity in various computer vision applications, and recently start to be deployed to resource-constrained mobile d…
Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based Approach
Haichuan Yang, Shupeng Gui, Yuhao Zhu +1
Deep Neural Networks (DNNs) are applied in a wide range of usecases. There is an increased demand for deploying DNNs on devices that do not have abundant resources such as memory a…
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Shupeng Gui, Haotao Wang, Chen Yu +3
Deep model compression has been extensively studied, and state-of-the-art methods can now achieve high compression ratios with minimal accuracy loss. This paper studies model compr…
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…
Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications
Carson Eisenach, Haichuan Yang, Ji Liu +1
Many complex domains, such as robotics control and real-time strategy (RTS) games, require an agent to learn a continuous control. In the former, an agent learns a policy over $\ma…