1.6k citations · 7.3k across the 231 of their papers we have counts for
58 papers · 1 filter
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better
Sameer Bibikar, Haris Vikalo, Zhangyang Wang +1
Federated learning (FL) enables distribution of machine learning workloads from the cloud to resource-limited edge devices. Unfortunately, current deep networks remain not only too…
Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search
Yifan Jiang, Xinyu Gong, Junru Wu +3
Efficient video architecture is the key to deploying video recognition systems on devices with limited computing resources. Unfortunately, existing video architectures are often co…
Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling
Ziyu Jiang, Tianlong Chen, Ting Chen +1
Contrastive learning approaches have achieved great success in learning visual representations with few labels of the target classes. That implies a tantalizing possibility of scal…
A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation
Wuyang Chen, Xianzhi Du, Fan Yang +8
This work presents a simple vision transformer design as a strong baseline for object localization and instance segmentation tasks. Transformers recently demonstrate competitive pe…
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods
Wenqing Zheng, Edward W Huang, Nikhil Rao +3
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in node classification, regression, and recommendation tasks. GNNs work well when rich and high-quality conn…
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models
Xuxi Chen, Tianlong Chen, Weizhu Chen +3
Gigantic pre-trained models have become central to natural language processing (NLP), serving as the starting point for fine-tuning towards a range of downstream tasks. However, tw…