71 citations · 139 across the 14 of their papers we have counts for
14 papers
Energy-Efficient GPU Clusters Scheduling for Deep Learning
Diandian Gu, Xintong Xie, Gang Huang +2
Training deep neural networks (DNNs) is a major workload in datacenters today, resulting in a tremendously fast growth of energy consumption. It is important to reduce the energy c…
Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation
Fangwen Wu, Jingxuan He, Yufei Yin +3
This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to highlight semantic regions in weakly supervised semantic segmentation. MCC adroitly draws ins…
Fast Distributed Inference Serving for Large Language Models
Bingyang Wu, Yinmin Zhong, Zili Zhang +6
Large language models (LLMs) power a new generation of interactive AI applications exemplified by ChatGPT. The interactive nature of these applications demands low latency for LLM…
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentation
Yang Lu, Pinxin Qian, Gang Huang +1
Personalized Federated Learning (PFL) aims to learn personalized models for each client based on the knowledge across all clients in a privacy-preserving manner. Existing PFL metho…
MuxFlow: Efficient and Safe GPU Sharing in Large-Scale Production Deep Learning Clusters
Yihao Zhao, Xin Liu, Shufan Liu +5
Large-scale GPU clusters are widely-used to speed up both latency-critical (online) and best-effort (offline) deep learning (DL) workloads. However, most DL clusters either dedicat…
Federated Semi-Supervised Learning with Annotation Heterogeneity
Xinyi Shang, Gang Huang, Yang Lu +4
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data. Most of the existing FSSL work…
Characterizing and Detecting WebAssembly Runtime Bugs
Yixuan Zhang, Shangtong Cao, Haoyu Wang +6
WebAssembly (abbreviated WASM) has emerged as a promising language of the Web and also been used for a wide spectrum of software applications such as mobile applications and deskto…
DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning
Jialiang Han, Yudong Han, Gang Huang +1
Federated learning (FL) is an emerging promising paradigm of privacy-preserving machine learning (ML). An important type of FL is cross-silo FL, which enables a small scale of orga…
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features
Xinyi Shang, Yang Lu, Gang Huang +1
Federated learning (FL) provides a privacy-preserving solution for distributed machine learning tasks. One challenging problem that severely damages the performance of FL models is…
Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning
Jialiang Han, Yun Ma, Yudong Han
Federated learning (FL) is an emerging promising privacy-preserving machine learning paradigm and has raised more and more attention from researchers and developers. FL keeps users…
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
Ningyu Zhang, Qianghuai Jia, Shumin Deng +8
Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search. Prior conceptual graph construction approaches typically extract high-…
Federated Mutual Learning
Tao Shen, Jie Zhang, Xinkang Jia +6
Federated learning (FL) enables collaboratively training deep learning models on decentralized data. However, there are three types of heterogeneities in FL setting bringing about…
Galaxy Learning -- A Position Paper
Chao Wu, Jun Xiao, Gang Huang +1
The recent rapid development of artificial intelligence (AI, mainly driven by machine learning research, especially deep learning) has achieved phenomenal success in various applic…
A Systematic Analysis of Fine-Grained Human Mobility Prediction with On-Device Contextual Data
Huoran Li
User mobility prediction is widely considered to be helpful for various sorts of location based services on mobile devices. A large amount of studies have explored different algori…