Search Results for author: Haichuan Yang

Found 21 papers, 9 papers with code

TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models

no code implementations5 Sep 2023 Yuan Shangguan, Haichuan Yang, Danni Li, Chunyang Wu, Yassir Fathullah, Dilin Wang, Ayushi Dalmia, Raghuraman Krishnamoorthi, Ozlem Kalinli, Junteng Jia, Jay Mahadeokar, Xin Lei, Mike Seltzer, Vikas Chandra

Results demonstrate that our TODM Supernet either matches or surpasses the performance of manually tuned models by up to a relative of 3% better in word error rate (WER), while efficiently keeping the cost of training many models at a small constant.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Learning a Dual-Mode Speech Recognition Model via Self-Pruning

no code implementations25 Jul 2022 Chunxi Liu, Yuan Shangguan, Haichuan Yang, Yangyang Shi, Raghuraman Krishnamoorthi, Ozlem Kalinli

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 and deployment for both use cases.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks

1 code implementation2 Jun 2022 Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan, Raghuraman Krishnamoorthi, Vikas Chandra, Yingyan Lin

Efficient deep neural network (DNN) models equipped with compact operators (e. g., depthwise convolutions) have shown great potential in reducing DNNs' theoretical complexity (e. g., the total number of weights/operations) while maintaining a decent model accuracy.

PyTorchVideo: A Deep Learning Library for Video Understanding

1 code implementation18 Nov 2021 Haoqi Fan, Tullie Murrell, Heng Wang, Kalyan Vasudev Alwala, Yanghao Li, Yilei Li, Bo Xiong, Nikhila Ravi, Meng Li, Haichuan Yang, Jitendra Malik, Ross Girshick, Matt Feiszli, Aaron Adcock, Wan-Yen Lo, Christoph Feichtenhofer

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 tasks, including classification, detection, self-supervised learning, and low-level processing.

Self-Supervised Learning Video Understanding

Low-Rank+Sparse Tensor Compression for Neural Networks

no code implementations2 Nov 2021 Cole Hawkins, Haichuan Yang, Meng Li, Liangzhen Lai, Vikas Chandra

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 Decomposition

GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework

2 code implementations ECCV 2020 Haotao Wang, Shupeng Gui, Haichuan Yang, Ji Liu, Zhangyang Wang

Generative adversarial networks (GANs) have gained increasing popularity in various computer vision applications, and recently start to be deployed to resource-constrained mobile devices.

Image-to-Image Translation Quantization +1

Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based Approach

1 code implementation CVPR 2020 Haichuan Yang, Shupeng Gui, Yuhao Zhu, Ji Liu

A key parameter that all existing compression techniques are sensitive to is the compression ratio (e. g., pruning sparsity, quantization bitwidth) of each layer.

Neural Network Compression Quantization

Model Compression with Adversarial Robustness: A Unified Optimization Framework

2 code implementations NeurIPS 2019 Shupeng Gui, Haotao Wang, Chen Yu, Haichuan Yang, Zhangyang Wang, Ji Liu

Deep model compression has been extensively studied, and state-of-the-art methods can now achieve high compression ratios with minimal accuracy loss.

Adversarial Robustness Model Compression +1

ECC: Platform-Independent Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model

2 code implementations CVPR 2019 Haichuan Yang, Yuhao Zhu, Ji Liu

The energy estimate model allows us to formulate DNN compression as a constrained optimization that minimizes the DNN loss function over the energy constraint.

Neural Network Compression regression

Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications

1 code implementation ICLR 2019 Carson Eisenach, Haichuan Yang, Ji Liu, Han Liu

In the former, an agent learns a policy over $\mathbb{R}^d$ and in the latter, over a discrete set of actions each of which is parametrized by a continuous parameter.

Continuous Control Reinforcement Learning (RL)

Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking

1 code implementation ICLR 2019 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 in real-time.

Learning Simple Thresholded Features with Sparse Support Recovery

no code implementations16 Apr 2018 Hongyu Xu, Zhangyang Wang, Haichuan Yang, Ding Liu, Ji Liu

The thresholded feature has recently emerged as an extremely efficient, yet rough empirical approximation, of the time-consuming sparse coding inference process.

Dictionary Learning

A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification

no code implementations18 Mar 2018 Ke Ren, Haichuan Yang, Yu Zhao, Mingshan Xue, Hongyu Miao, Shuai Huang, Ji Liu

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 few samples labeled as "positive" together with a large volume of "unlabeled" samples that may contain both positive and negative samples.

EEG feature selection +5

On The Projection Operator to A Three-view Cardinality Constrained Set

no code implementations ICML 2017 Haichuan Yang, Shupeng Gui, Chuyang Ke, Daniel Stefankovic, Ryohei Fujimaki, Ji Liu

The cardinality constraint is an intrinsic way to restrict the solution structure in many domains, for example, sparse learning, feature selection, and compressed sensing.

feature selection Sparse Learning

On Benefits of Selection Diversity via Bilevel Exclusive Sparsity

no code implementations CVPR 2016 Haichuan Yang, Yijun Huang, Lam Tran, Ji Liu, Shuai Huang

In this paper, we proposed a general bilevel exclusive sparsity formulation to pursue the diversity by restricting the overall sparsity and the sparsity in each group.

feature selection Image Classification

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