25 citations · 30 across the 7 of their papers we have counts for
7 papers
Epistemic Uncertainty Quantification For Pre-trained Neural Network
Hanjing Wang, Qiang Ji
Epistemic uncertainty quantification (UQ) identifies where models lack knowledge. Traditional UQ methods, often based on Bayesian neural networks, are not suitable for pre-trained…
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
Hanjing Wang, Man-Kit Sit, Congjie He +5
This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed to perform scalable reinforcement learning (RL) with large sequence models (such as transf…
Body Knowledge and Uncertainty Modeling for Monocular 3D Human Body Reconstruction
Yufei Zhang, Hanjing Wang, Jeffrey O. Kephart +1
While 3D body reconstruction methods have made remarkable progress recently, it remains difficult to acquire the sufficiently accurate and numerous 3D supervisions required for tra…
Large Sequence Models for Sequential Decision-Making: A Survey
Muning Wen, Runji Lin, Hanjing Wang +6
Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer visio…
Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning
Hanjing Wang, Dhiraj Joshi, Shiqiang Wang +1
Predictions made by deep learning models are prone to data perturbations, adversarial attacks, and out-of-distribution inputs. To build a trusted AI system, it is therefore critica…
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Ming Zhou, Ziyu Wan, Hanjing Wang +6
Population-based multi-agent reinforcement learning (PB-MARL) refers to the series of methods nested with reinforcement learning (RL) algorithms, which produces a self-generated se…
Towards Efficient Discrete Integration via Adaptive Quantile Queries
Fan Ding, Hanjing Wang, Ashish Sabharwal +1
Discrete integration in a high dimensional space of n variables poses fundamental challenges. The WISH algorithm reduces the intractable discrete integration problem into n optimiz…