3.7k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of SciencesCN257 papers
- Beijing Academy of Quantum Information SciencesCN235 papers
- Peking UniversityCN220 papers
- University of Science and Technology of ChinaCN204 papers
- Tsinghua UniversityCN193 papers
- University of Chinese Academy of SciencesCN143 papers
- University of Hong KongHK135 papers
- Institute of PhysicsPL94 papers
- National University of SingaporeSG94 papers
- Peng Cheng LaboratoryCN94 papers
- Hong Kong University of Science and TechnologyHK93 papers
- Fudan UniversityCN90 papers
9 papers · 2 filters
Expanding Horizons of Level Diversity via Multi-objective Evolutionary Learning
Qingquan Zhang, Ziqi Wang, Yuchen Li +3
In recent years, the generation of diverse game levels has gained increasing interest, contributing to a richer and more engaging gaming experience. A number of level diversity met…
Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning
Xudong Wang, Guoming Tang, Junyu Xue +3
Non-Intrusive Load Monitoring (NILM) offers a cost-effective method to obtain fine-grained appliance-level energy consumption in smart homes and building applications. However, the…
Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems
Hiroki Shiraishi, Yohei Hayamizu, Tomonori Hashiyama +3
Rule representations significantly influence the search capabilities and decision boundaries within the search space of Learning Classifier Systems (LCSs), a family of rule-based m…
h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objective
Wenjian Huang, Guiping Cao, Jiahao Xia +3
Deep neural networks have demonstrated remarkable performance across numerous learning tasks but often suffer from miscalibration, resulting in unreliable probability outputs. This…
A Class Inference Scheme With Dempster-Shafer Theory for Learning Fuzzy-Classifier Systems
Hiroki Shiraishi, Hisao Ishibuchi, Masaya Nakata
The decision-making process significantly influences the predictions of machine learning models. This is especially important in rule-based systems such as Learning Fuzzy-Classifie…
X-KAN: Optimizing Local Kolmogorov-Arnold Networks via Evolutionary Rule-Based Machine Learning
Hiroki Shiraishi, Hisao Ishibuchi, Masaya Nakata
Function approximation is a critical task in various fields. However, existing neural network approaches struggle with locally complex or discontinuous functions due to their relia…
FedAGHN: Personalized Federated Learning with Attentive Graph HyperNetworks
Jiarui Song, Yunheng Shen, Chengbin Hou +4
Personalized Federated Learning (PFL) aims to address the statistical heterogeneity of data across clients by learning the personalized model for each client. Among various PFL app…
Hardware-Aware DNN Compression for Homogeneous Edge Devices
Kunlong Zhang, Guiying Li, Ning Lu +2
Deploying deep neural networks (DNNs) across homogeneous edge devices (the devices with the same SKU labeled by the manufacturer) often assumes identical performance among them. Ho…
MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework
Ping Guo, Cheng Gong, Xi Lin +4
Crafting adversarial examples is crucial for evaluating and enhancing the robustness of Deep Neural Networks (DNNs), presenting a challenge equivalent to maximizing a non-different…