research
∙
09/05/2023
TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models
Automatic Speech Recognition (ASR) models need to be optimized for speci...
share
research
∙
06/08/2023
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts
Weight-sharing supernet has become a vital component for performance est...
research
∙
11/09/2022
LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting
This paper proposes a hardware-efficient architecture, Linearized Convol...
research
∙
07/25/2022
Learning a Dual-Mode Speech Recognition Model via Self-Pruning
There is growing interest in unifying the streaming and full-context aut...
research
∙
06/02/2022
DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks
Efficient deep neural network (DNN) models equipped with compact operato...
research
∙
11/18/2021
PyTorchVideo: A Deep Learning Library for Video Understanding
We introduce PyTorchVideo, an open-source deep-learning library that pro...
research
∙
11/02/2021
Low-Rank+Sparse Tensor Compression for Neural Networks
Low-rank tensor compression has been proposed as a promising approach to...
research
∙
10/15/2021
Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet
From wearables to powerful smart devices, modern automatic speech recogn...
research
∙
07/09/2021
Noisy Training Improves E2E ASR for the Edge
Automatic speech recognition (ASR) has become increasingly ubiquitous on...
research
∙
08/25/2020
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
Generative adversarial networks (GANs) have gained increasing popularity...
research
∙
10/14/2019
Learning Sparsity and Quantization Jointly and Automatically for Neural Network Compression via Constrained Optimization
Deep Neural Networks (DNNs) are widely applied in a wide range of usecas...
research
∙
02/10/2019
Adversarially Trained Model Compression: When Robustness Meets Efficiency
The robustness of deep models to adversarial attacks has gained signific...
research
∙
12/05/2018
ECC: Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model
Many DNN-enabled vision applications constantly operate under severe ene...
research
∙
06/13/2018
Marginal Policy Gradients for Complex Control
Many complex domains, such as robotics control and real-time strategy (R...
research
∙
06/12/2018
End-to-End Learning of Energy-Constrained Deep Neural Networks
Deep Neural Networks (DNN) are increasingly deployed in highly energy-co...
research
∙
04/16/2018
Learning Simple Thresholded Features with Sparse Support Recovery
The thresholded feature has recently emerged as an extremely efficient, ...
research
∙
03/18/2018
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification
The positive-unlabeled (PU) classification is a common scenario in real-...
research
∙
03/21/2017
