Feng Chen

Design of the arm-wrestling robot’s force acquisition system based on Qt

2017 · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

As a collection of entertainment and medical rehabilitation in a robot, the research on the arm-wrestling robot is of great significance. In order to achieve the collection of the arm-wrestling robot’s force signals, the design …

Quantifying Classification Uncertainty using Regularized Evidential Neural Networks

2019 · arXiv (Cornell University)

Traditional deep neural nets (NNs) have shown the state-of-the-art performance in the task of classification in various applications. However, NNs have not considered any types of uncertainty associated with the class probabilities to minimize risk …

Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning.

2020 · neural information processing systems

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i.e., the goal space, is …

A Nested Bi-level Optimization Framework for Robust Few Shot Learning

2022 · Proceedings of the AAAI Conference on Artificial Intelligence

Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal.Hence, it fails to address the domain shift between base and novel classes in …

Security of railway control systems: A survey, research issues and challenges

2022 · High-speed Railway

With the rapid development of railway transportation, higher requirements for capacity are increasing and amount of communication, computer and control technologies are applied in train control systems which is the popular method for train control. …

Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization

2023 · arXiv (Cornell University)

Deep neural networks have achieved significant success in the last decades, but they are not well-calibrated and often produce unreliable predictions. A large number of literature relies on uncertainty quantification to evaluate the reliability of …

Fairness across domains: a unified fairness-aware framework for domain generalization and unsupervised adaptation

2026 · Frontiers in Big Data

Fairness in machine learning remains a critical challenge, particularly in the presence of domain shift. We propose a unified fairness-aware framework for both domain generalization (DG) and unsupervised domain adaptation (UDA), which jointly addresses domain …