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
76 papers · 1 filter
Kolmogorov-Arnold Classifier Systems as Universal Approximators
Hiroki Shiraishi, Hisao Ishibuchi, Masaya Nakata
As the input dimension grows, rule-based machine learning, such as Learning Classifier Systems (LCSs), faces a fundamental scalability bottleneck for function approximation: bo…
SIMS: Scale-Invariant Merit-Function-Based Scalarization for Multi-Task Learning
Zebin Chen, Fei Xing, Yang Chen +4
Multi-task learning (MTL) requires navigating unavoidable trade-offs among competing objectives. This paradigm is frequently formulated as multi-objective optimization (MOO), where…
Interpretable and Fair Generalized Additive Neural Networks via Multi-objective Learning
Ziming Wang, Changwu Huang, Ke Tang +2
Interpretability and fairness are two of the most emphasized dimensions in trustworthy artificial intelligence (AI). Various explainable AI methods have been introduced to improve…
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…
CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing
Zijian Zhao, Fanyi Meng, Zhonghao Lyu +3
Channel state information (CSI) is a fundamental component in both wireless communication and sensing systems, enabling critical functions such as radio resource optimization and e…
PiLocNet: Physics-informed neural network on 3D localization with rotating point spread function
Mingda Lu, Zitian Ao, Chao Wang +2
For the 3D localization problem using point spread function (PSF) engineering, we propose a novel enhancement of our previously introduced localization neural network, LocNet. The…
Sinc Kolmogorov-Arnold network and its application for solving PDEs with singularities
Tianchi Yu, Jingwei Qiu, Jiang Yang +1
In this paper, we propose to use Sinc interpolation in the context of Kolmogorov-Arnold Networks, neural networks with learnable activation functions, which recently gained attenti…
Fairness-aware Multiobjective Evolutionary Learning
Qingquan Zhang, Jialin Liu, Xin Yao
Multiobjective evolutionary learning (MOEL) has demonstrated its advantages of training fairer machine learning models considering a predefined set of conflicting objectives, inclu…
Spectral Informed Neural Network: An Efficient and Low-Memory PINN
Tianchi Yu, Yiming Qi, Ivan Oseledets +1
With growing investigations into solving partial differential equations by physics-informed neural networks (PINNs), more accurate and efficient PINNs are required to meet the prac…
LLM-Barber: Block-Aware Rebuilder for Sparsity Mask in One-Shot for Large Language Models
Yupeng Su, Ziyi Guan, Xiaoqun Liu +6
Large language models (LLMs) have seen substantial growth, necessitating efficient model pruning techniques. Existing post-training pruning methods primarily measure weight importa…
Procedural Fairness in Machine Learning
Ziming Wang, Changwu Huang, Ke Tang +1
Fairness in machine learning (ML) has garnered significant attention. However, current research has mainly concentrated on the distributive fairness of ML models, with limited focu…
TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic
Zezheng Feng, Yifan Jiang, Hongjun Wang +5
Recent achievements in deep learning (DL) have shown its potential for predicting traffic flows. Such predictions are beneficial for understanding the situation and making decision…
APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models
Ziyi Guan, Hantao Huang, Yupeng Su +3
Large Language Models (LLMs) have greatly advanced the natural language processing paradigm. However, the high computational load and huge model sizes pose a grand challenge for de…
Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting
Pengxin Guo, Pengrong Jin, Ziyue Li +2
Accurate spatial-temporal traffic flow forecasting is crucial in aiding traffic managers in implementing control measures and assisting drivers in selecting optimal travel routes.…
Towards A Flexible Accuracy-Oriented Deep Learning Module Inference Latency Prediction Framework for Adaptive Optimization Algorithms
Jingran Shen, Nikos Tziritas, Georgios Theodoropoulos
With the rapid development of Deep Learning, more and more applications on the cloud and edge tend to utilize large DNN (Deep Neural Network) models for improved task execution eff…
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset
Weijie Liu, Xiaoxi Zhang, Jingpu Duan +3
Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data. While prior works…