682 citations · 2.5k across the 94 of their papers we have counts for
10 papers · 1 filter
Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling
Xiucheng Wang, Longfei Ma, Haocheng Li +3
Task scheduling is a critical problem when one user offloads multiple different tasks to the edge server. When a user has multiple tasks to offload and only one task can be transmi…
Task Adaptive Parameter Sharing for Multi-Task Learning
Matthew Wallingford, Hao Li, Alessandro Achille +4
Adapting pre-trained models with broad capabilities has become standard practice for learning a wide range of downstream tasks. The typical approach of fine-tuning different models…
Dash: Semi-Supervised Learning with Dynamic Thresholding
Yi Xu, Lei Shang, Jinxing Ye +5
While semi-supervised learning (SSL) has received tremendous attentions in many machine learning tasks due to its successful use of unlabeled data, existing SSL algorithms use eith…
Importance Weighted Adversarial Discriminative Transfer for Anomaly Detection
Cangning Fan, Fangyi Zhang, Peng Liu +5
Previous transfer methods for anomaly detection generally assume the availability of labeled data in source or target domains. However, such an assumption is not valid in most real…
Why Does Multi-Epoch Training Help?
Yi Xu, Qi Qian, Hao Li +1
Stochastic gradient descent (SGD) has become the most attractive optimization method in training large-scale deep neural networks due to its simplicity, low computational cost in e…
A Theoretical Analysis of Learning with Noisily Labeled Data
Yi Xu, Qi Qian, Hao Li +1
Noisy labels are very common in deep supervised learning. Although many studies tend to improve the robustness of deep training for noisy labels, rare works focus on theoretically…
Large-scale Distance Metric Learning with Uncertainty
Qi Qian, Jiasheng Tang, Hao Li +2
Distance metric learning (DML) has been studied extensively in the past decades for its superior performance with distance-based algorithms. Most of the existing methods propose to…
Robust Optimization over Multiple Domains
Qi Qian, Shenghuo Zhu, Jiasheng Tang +3
In this work, we study the problem of learning a single model for multiple domains. Unlike the conventional machine learning scenario where each domain can have the corresponding m…
Adaptive Consensus ADMM for Distributed Optimization
Zheng Xu, Gavin Taylor, Hao Li +3
The alternating direction method of multipliers (ADMM) is commonly used for distributed model fitting problems, but its performance and reliability depend strongly on user-defined…
Training Quantized Nets: A Deeper Understanding
Hao Li, Soham De, Zheng Xu +3
Currently, deep neural networks are deployed on low-power portable devices by first training a full-precision model using powerful hardware, and then deriving a corresponding low-p…