58 citations · 120 across the 14 of their papers we have counts for
14 papers
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts
Ganesh Jawahar, Haichuan Yang, Yunyang Xiong +10
Weight-sharing supernets are crucial for performance estimation in cutting-edge neural architecture search (NAS) frameworks. Despite their ability to generate diverse subnetworks w…
Learning a Dual-Mode Speech Recognition Model via Self-Pruning
Chunxi Liu, Yuan Shangguan, Haichuan Yang +3
There is growing interest in unifying the streaming and full-context automatic speech recognition (ASR) networks into a single end-to-end ASR model to simplify the model training a…
PyTorchVideo: A Deep Learning Library for Video Understanding
Haoqi Fan, Tullie Murrell, Heng Wang +13
We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tas…
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…
Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet
Haichuan Yang, Yuan Shangguan, Dilin Wang +6
From wearables to powerful smart devices, modern automatic speech recognition (ASR) models run on a variety of edge devices with different computational budgets. To navigate the Pa…
Noisy Training Improves E2E ASR for the Edge
Dilin Wang, Yuan Shangguan, Haichuan Yang +6
Automatic speech recognition (ASR) has become increasingly ubiquitous on modern edge devices. Past work developed streaming End-to-End (E2E) all-neural speech recognizers that can…
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…
Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking
Haichuan Yang, Yuhao Zhu, Ji Liu
Deep Neural Networks (DNNs) are increasingly deployed in highly energy-constrained environments such as autonomous drones and wearable devices while at the same time must operate i…
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification
Ke Ren, Haichuan Yang, Yu Zhao +4
The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a f…
On The Projection Operator to A Three-view Cardinality Constrained Set
Haichuan Yang, Shupeng Gui, Chuyang Ke +3
The cardinality constraint is an intrinsic way to restrict the solution structure in many domains, for example, sparse learning, feature selection, and compressed sensing. To solve…