72 citations · 123 across the 5 of their papers we have counts for
6 papers
MViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design
Hanxue Liang, Zhiwen Fan, Rishov Sarkar +6
Multi-task learning (MTL) encapsulates multiple learned tasks in a single model and often lets those tasks learn better jointly. However, when deploying MTL onto those real-world s…
Self-Damaging Contrastive Learning
Ziyu Jiang, Tianlong Chen, Bobak Mortazavi +1
The recent breakthrough achieved by contrastive learning accelerates the pace for deploying unsupervised training on real-world data applications. However, unlabeled data in realit…
Robust Pre-Training by Adversarial Contrastive Learning
Ziyu Jiang, Tianlong Chen, Ting Chen +1
Recent work has shown that, when integrated with adversarial training, self-supervised pre-training can lead to state-of-the-art robustness In this work, we improve robustness-awar…
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings
Yue Wang, Ziyu Jiang, Xiaohan Chen +4
Convolutional neural networks (CNNs) have been increasingly deployed to edge devices. Hence, many efforts have been made towards efficient CNN inference in resource-constrained pla…
ArcticNet: A Deep Learning Solution to Classify Arctic Wetlands
Ziyu Jiang, Kate Von Ness, Julie Loisel +1
Arctic environments are rapidly changing under the warming climate. Of particular interest are wetlands, a type of ecosystem that constitutes the most effective terrestrial long-te…
Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images
Wuyang Chen, Ziyu Jiang, Zhangyang Wang +2
Segmentation of ultra-high resolution images is increasingly demanded, yet poses significant challenges for algorithm efficiency, in particular considering the (GPU) memory limits.…