Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of SciencesCN2.9k papers
- Centre National de la Recherche ScientifiqueFR2.7k papers
- Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di FrascatiIT2.6k papers
- Institute of High Energy PhysicsCN2.6k papers
- University of Science and Technology of ChinaCN2.5k papers
- Peking UniversityCN2.4k papers
- University of Chinese Academy of SciencesCN2.3k papers
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma IIT2.3k papers
- Institut National de Physique Nucléaire et de Physique des ParticulesFR2.3k papers
- Rutherford Appleton LaboratoryGB2.2k papers
- Istituto Nazionale di Fisica Nucleare, Sezione di GenovaIT2.2k papers
- European Organization for Nuclear ResearchCH2.2k papers
36 papers · 2 filters
Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference
Hongzheng Chen, Jiahao Zhang, Yixiao Du +5
Recent advancements in large language models (LLMs) boasting billions of parameters have generated a significant demand for efficient deployment in inference workloads. The majorit…
Deep sub-ensembles meets quantile regression: uncertainty-aware imputation for time series
Ying Liu, Peng Cui, Wenbo Hu +1
Real-world time series data often exhibits substantial missing values, posing challenges for advanced analysis. A common approach to addressing this issue is imputation, where the…
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction
Guangyin Jin, Lingbo Liu, Fuxian Li +1
Traffic congestion event prediction is an important yet challenging task in intelligent transportation systems. Many existing works about traffic prediction integrate various tempo…
Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing
Rongwei Lu, Yutong Jiang, Yinan Mao +4
Federated Learning (FL) in mobile environments faces significant communication bottlenecks. Gradient compression has proven as an effective solution to this issue, offering substan…
Positive-Unlabeled Node Classification with Structure-aware Graph Learning
Hansi Yang, Yongqi Zhang, Quanming Yao +1
Node classification on graphs is an important research problem with many applications. Real-world graph data sets may not be balanced and accurate as assumed by most existing works…
Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt
Zihao Zhao, Zhenpeng Shi, Yang Liu +1
Federated Learning (FL) is often impeded by communication overhead issues. Prompt tuning, as a potential solution, has been introduced to only adjust a few trainable parameters rat…
Learning Interpretable Rules for Scalable Data Representation and Classification
Zhuo Wang, Wei Zhang, Ning Liu +1
Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. Howe…
Distributional Soft Actor-Critic with Three Refinements
Jingliang Duan, Wenxuan Wang, Liming Xiao +6
Reinforcement learning (RL) has shown remarkable success in solving complex decision-making and control tasks. However, many model-free RL algorithms experience performance degrada…
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction
Shiguang Wu, Yaqing Wang, Quanming Yao
Molecular property prediction (MPP) plays a crucial role in biomedical applications, but it often encounters challenges due to a scarcity of labeled data. Existing works commonly a…
Predicting Fatigue Crack Growth via Path Slicing and Re-Weighting
Yingjie Zhao, Yong Liu, Zhiping Xu
Predicting potential risks associated with the fatigue of key structural components is crucial in engineering design. However, fatigue often involves entangled complexities of mate…
A Spatiotemporal Deep Neural Network for Fine-Grained Multi-Horizon Wind Prediction
Fanling Huang, Yangdong Deng
The prediction of wind in terms of both wind speed and direction, which has a crucial impact on many real-world applications like aviation and wind power generation, is extremely c…
TCGAN: Convolutional Generative Adversarial Network for Time Series Classification and Clustering
Fanling Huang, Yangdong Deng
Recent works have demonstrated the superiority of supervised Convolutional Neural Networks (CNNs) in learning hierarchical representations from time series data for successful clas…
PRISM: Mitigating EHR Data Sparsity via Learning from Missing Feature Calibrated Prototype Patient Representations
Yinghao Zhu, Zixiang Wang, Long He +4
Electronic Health Records (EHRs) contain a wealth of patient data; however, the sparsity of EHRs data often presents significant challenges for predictive modeling. Conventional im…
PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model Modification
Yizhen Yuan, Rui Kong, Shenghao Xie +2
Backdoor attack is a major threat to deep learning systems in safety-critical scenarios, which aims to trigger misbehavior of neural network models under attacker-controlled condit…
A Multilayer Perceptron-based Fast Sunlight Assessment for the Conceptual Design of Residential Neighborhoods under Chinese Policy
Can Jiang, Xiong Liang, Yu-Cheng Zhou +6
In Chinese building codes, it is required that residential buildings receive a minimum number of hours of natural, direct sunlight on a specified winter day, which represents the w…
Learning Specialized Activation Functions for Physics-informed Neural Networks
Honghui Wang, Lu Lu, Shiji Song +1
Physics-informed neural networks (PINNs) are known to suffer from optimization difficulty. In this work, we reveal the connection between the optimization difficulty of PINNs and a…
Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence
Liyuan Wang, Xingxing Zhang, Qian Li +4
Continual learning aims to empower artificial intelligence (AI) with strong adaptability to the real world. For this purpose, a desirable solution should properly balance memory st…
DeSCo: Towards Generalizable and Scalable Deep Subgraph Counting
Tianyu Fu, Chiyue Wei, Yu Wang +1
We introduce DeSCo, a scalable neural deep subgraph counting pipeline, designed to accurately predict both the count and occurrence position of queries on target graphs post single…
Graph Contrastive Learning with Generative Adversarial Network
Cheng Wu, Chaokun Wang, Jingcao Xu +5
Graph Neural Networks (GNNs) have demonstrated promising results on exploiting node representations for many downstream tasks through supervised end-to-end training. To deal with t…
HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting
Zezhi Shao, Fei Wang, Tao Sun +7
Traffic forecasting, which aims to predict traffic conditions based on historical observations, has been an enduring research topic and is widely recognized as an essential compone…
SageFormer: Series-Aware Framework for Long-term Multivariate Time Series Forecasting
Zhenwei Zhang, Linghang Meng, Yuantao Gu
In the burgeoning ecosystem of Internet of Things, multivariate time series (MTS) data has become ubiquitous, highlighting the fundamental role of time series forecasting across nu…
Unlocking the Potential of Deep Learning in Peak-Hour Series Forecasting
Zhenwei Zhang, Xin Wang, Jingyuan Xie +2
Unlocking the potential of deep learning in Peak-Hour Series Forecasting (PHSF) remains a critical yet underexplored task in various domains. While state-of-the-art deep learning m…
Spatio-temporal Diffusion Point Processes
Yuan Yuan, Jingtao Ding, Chenyang Shao +2
Spatio-temporal point process (STPP) is a stochastic collection of events accompanied with time and space. Due to computational complexities, existing solutions for STPPs compromis…
Structure-Aware DropEdge Towards Deep Graph Convolutional Networks
Jiaqi Han, Wenbing Huang, Yu Rong +3
It has been discovered that Graph Convolutional Networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why…