71 citations · 131 across the 14 of their papers we have counts for
18 papers
General-Purpose Multi-Modal OOD Detection Framework
Viet Duong, Qiong Wu, Zhengyi Zhou +5
Out-of-distribution (OOD) detection identifies test samples that differ from the training data, which is critical to ensuring the safety and reliability of machine learning (ML) sy…
Scalable Neural Symbolic Regression using Control Variables
Xieting Chu, Hongjue Zhao, Enze Xu +3
Symbolic regression (SR) is a powerful technique for discovering the analytical mathematical expression from data, finding various applications in natural sciences due to its good…
Condensed Prototype Replay for Class Incremental Learning
Jiangtao Kong, Zhenyu Zong, Tianyi Zhou +1
Incremental learning (IL) suffers from catastrophic forgetting of old tasks when learning new tasks. This can be addressed by replaying previous tasks' data stored in a memory, whi…
Balancing Privacy Protection and Interpretability in Federated Learning
Zhe Li, Honglong Chen, Zhichen Ni +1
Federated learning (FL) aims to collaboratively train the global model in a distributed manner by sharing the model parameters from local clients to a central server, thereby poten…
Pre-Training Representations of Binary Code Using Contrastive Learning
Yifan Zhang, Chen Huang, Yueke Zhang +3
Binary code analysis and comprehension is critical to applications in reverse engineering and computer security tasks where source code is not available. Unfortunately, unlike sour…
Phy-Taylor: Physics-Model-Based Deep Neural Networks
Yanbing Mao, Lui Sha, Huajie Shao +3
Purely data-driven deep neural networks (DNNs) applied to physical engineering systems can infer relations that violate physics laws, thus leading to unexpected consequences. To ad…