97 citations · 312 across the 45 of their papers we have counts for
17 papers · 1 filter
Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks
Qihan Huang, Mengqi Xue, Wenqi Huang +4
Part-prototype networks (e.g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-inter…
Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition
Shunyu Liu, Yihe Zhou, Jie Song +5
Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit…
A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges
Yunpeng Qing, Shunyu Liu, Jie Song +4
Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of d…
Attention Diversification for Domain Generalization
Rang Meng, Xianfeng Li, Weijie Chen +7
Convolutional neural networks (CNNs) have demonstrated gratifying results at learning discriminative features. However, when applied to unseen domains, state-of-the-art models are…
A Survey of Neural Trees
Haoling Li, Jie Song, Mengqi Xue +4
Neural networks (NNs) and decision trees (DTs) are both popular models of machine learning, yet coming with mutually exclusive advantages and limitations. To bring the best of the…
ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition
Mengqi Xue, Qihan Huang, Haofei Zhang +4
Prototypical part network (ProtoPNet) has drawn wide attention and boosted many follow-up studies due to its self-explanatory property for explainable artificial intelligence (XAI)…
Federated Selective Aggregation for Knowledge Amalgamation
Donglin Xie, Ruonan Yu, Gongfan Fang +5
In this paper, we explore a new knowledge-amalgamation problem, termed Federated Selective Aggregation (FedSA). The goal of FedSA is to train a student model for a new task with th…
Learning with Recoverable Forgetting
Jingwen Ye, Yifang Fu, Jie Song +5
Life-long learning aims at learning a sequence of tasks without forgetting the previously acquired knowledge. However, the involved training data may not be life-long legitimate du…
Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning
Shunyu Liu, Jie Song, Yihe Zhou +4
Deep cooperative multi-agent reinforcement learning has demonstrated its remarkable success over a wide spectrum of complex control tasks. However, recent advances in multi-agent l…
Ask-AC: An Initiative Advisor-in-the-Loop Actor-Critic Framework
Shunyu Liu, Kaixuan Chen, Na Yu +3
Despite the promising results achieved, state-of-the-art interactive reinforcement learning schemes rely on passively receiving supervision signals from advisor experts, in the for…
Slimmable Domain Adaptation
Rang Meng, Weijie Chen, Shicai Yang +7
Vanilla unsupervised domain adaptation methods tend to optimize the model with fixed neural architecture, which is not very practical in real-world scenarios since the target data…
Label Matching Semi-Supervised Object Detection
Binbin Chen, Weijie Chen, Shicai Yang +6
Semi-supervised object detection has made significant progress with the development of mean teacher driven self-training. Despite the promising results, the label mismatch problem…
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System
Kaixuan Chen, Shunyu Liu, Na Yu +5
The real-time transient stability assessment (TSA) plays a critical role in the secure operation of the power system. Although the classic numerical integration method, \textit{i.e…
Spot-adaptive Knowledge Distillation
Jie Song, Ying Chen, Jingwen Ye +1
Knowledge distillation (KD) has become a well established paradigm for compressing deep neural networks. The typical way of conducting knowledge distillation is to train the studen…
Meta-attention for ViT-backed Continual Learning
Mengqi Xue, Haofei Zhang, Jie Song +1
Continual learning is a longstanding research topic due to its crucial role in tackling continually arriving tasks. Up to now, the study of continual learning in computer vision is…
Knowledge Amalgamation for Object Detection with Transformers
Haofei Zhang, Feng Mao, Mengqi Xue +4
Knowledge amalgamation (KA) is a novel deep model reusing task aiming to transfer knowledge from several well-trained teachers to a multi-talented and compact student. Currently, m…
Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction
Zipeng Zhong, Jie Song, Zunlei Feng +6
Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis. A popular computational paradigm formulates…