Better Knowledge Retention through Metric Learning
Simon Fraser University（西蒙弗雷泽大学）；Google（谷歌）；Institute for Advanced Study（高等研究院）；University of California San Diego（加州大学圣地亚哥分校）；Stanford University（斯坦福大学）；University of California, Berkeley（加州大学伯克利分校）
Ke Li, Shichong Peng, Kailas Vodrahalli, Jitendra Malik
In continual learning, new categories may be introduced over time, and an ideal learning system should perform well on both the original categories and the new categories. While deep neural nets have achieved resounding success in the classical supervised setting, they are known to forget about knowledge acquired in prior episodes of learning if the examples encountered in the current episode of learning are drastically different from those encountered in prior episodes. In this paper, we propose a new method that can both leverage the expressive power of deep neural nets and is resilient to forgetting when new categories are introduced. We found the proposed method can reduce forgetting by 2.3x to 6.9x on CIFAR-10 compared to existing methods and by 1.8x to 2.7x on ImageNet compared to an oracle baseline.
Multi-relational Graph Diffusion Neural Network with Parallel Retention for Stock Trends Classification
School of Computer Science, University of Bristol（布里斯托大学计算机科学学院）；School of Engineering Mathematics and Technology, University of Bristol（布里斯托大学工程数学与技术学院）
SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Chongqing University（重庆大学）；Peng Cheng Laboratory（鹏城实验室）；Wuhan AI Research（武汉人工智能研究院）
Naloxone, an opioid antagonist, has been widely used to save lives from opioid overdose, a leading cause for death in the opioid epidemic. However, naloxone has short brain retention ability, which limits its therapeutic efficacy. Developing better opioid antagonists is critical in combating the opioid epidemic.Instead of exhaustively searching in a huge chemical space for better opioid antagonists, we adopt reinforcement learning which allows efficient gradient-based search towards molecules with desired physicochemical and/or biological properties. Specifically, we implement a deep reinforce…
Looking through the past: better knowledge retention for generative replay in continual learning
IDEAS NCBR；Gdańsk University of Technology（格但斯克工业大学）；Warsaw University of Technology（华沙工业大学）；Jagiellonian University（雅盖隆大学）；Tooploox；Computer Vision Center（计算机视觉中心）；Universitat Autònoma Barcelona（巴塞罗那自治大学）
Retention Time Prediction for Chromatographic Enantioseparation by Quantile Geometry-enhanced Graph Neural Network
School of Materials Science and Engineering, Peking University（北京大学材料科学与工程学院）；College of Engineering, Peking University（北京大学工学院）；Peng Cheng Laboratory（鹏城实验室）；Southern University of Science and Technology（南方科技大学）
Harmony: A Unified Framework for Modality Incremental Learning
Peng Cheng Laboratory（鹏城实验室）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）
