25 citations · 30 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…