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
117 papers · 2 filters
Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
Qingsong Lv, Ming Ding, Qiang Liu +7
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understandi…
Reversible Upper Confidence Bound Algorithm to Generate Diverse Optimized Candidates
Bin Chong, Yingguang Yang, Zi-Le Wang +2
Most algorithms for the multi-armed bandit problem in reinforcement learning aimed to maximize the expected reward, which are thus useful in searching the optimized candidate with…
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing
Tianfeng Liu, Yangrui Chen, Dan Li +7
Graph neural networks (GNNs) have extended the success of deep neural networks (DNNs) to non-Euclidean graph data, achieving ground-breaking performance on various tasks such as no…
Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and Classification
Hongyi Yuan, Sheng Yu
Medical automatic diagnosis aims to imitate human doctors in real-world diagnostic processes and to achieve accurate diagnoses by interacting with the patients. The task is formula…
Achieving an Accurate Random Process Model for PV Power using Cheap Data: Leveraging the SDE and Public Weather Reports
Yiwei Qiu, Jin Lin, Zhipeng Zhou +3
The stochastic differential equation (SDE)-based random process models of volatile renewable energy sources (RESs) jointly capture the evolving probability distribution and tempora…
KNAS: Green Neural Architecture Search
Jingjing Xu, Liang Zhao, Junyang Lin +3
Many existing neural architecture search (NAS) solutions rely on downstream training for architecture evaluation, which takes enormous computations. Considering that these computat…
Network representation learning: A macro and micro view
Xueyi Liu, Jie Tang
Graph is a universe data structure that is widely used to organize data in real-world. Various real-word networks like the transportation network, social and academic network can b…
Efficient Neural Network Training via Forward and Backward Propagation Sparsification
Xiao Zhou, Weizhong Zhang, Zonghao Chen +2
Sparse training is a natural idea to accelerate the training speed of deep neural networks and save the memory usage, especially since large modern neural networks are significantl…
Safe Policy Optimization with Local Generalized Linear Function Approximations
Akifumi Wachi, Yunyue Wei, Yanan Sui
Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems. Existing safe exploration methods guaranteed safety under the assumption of regularity…
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning
Qinkai Zheng, Xu Zou, Yuxiao Dong +5
Adversarial attacks on graphs have posed a major threat to the robustness of graph machine learning (GML) models. Naturally, there is an ever-escalating arms race between attackers…
AFEC: Active Forgetting of Negative Transfer in Continual Learning
Liyuan Wang, Mingtian Zhang, Zhongfan Jia +5
Continual learning aims to learn a sequence of tasks from dynamic data distributions. Without accessing to the old training samples, knowledge transfer from the old tasks to each n…
DAPPER: Label-Free Performance Estimation after Personalization for Heterogeneous Mobile Sensing
Taesik Gong, Yewon Kim, Adiba Orzikulova +4
Many applications utilize sensors in mobile devices and machine learning to provide novel services. However, various factors such as different users, devices, and environments impa…
Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability
Yan Kang, Yang Liu, Yuezhou Wu +2
We present a novel privacy-preserving federated adversarial domain adaptation approach () to address an under-studied but practical cross-silo federated domain adap…
Data-driven discoveries of Bäcklund transforms and soliton evolution equations via deep neural network learning schemes
Zijian Zhou, Li Wang, Weifang Weng +1
We introduce a deep neural network learning scheme to learn the Bäcklund transforms (BTs) of soliton evolution equations and an enhanced deep learning scheme for data-driven solito…
Learning Domain Invariant Representations in Goal-conditioned Block MDPs
Beining Han, Chongyi Zheng, Harris Chan +3
Deep Reinforcement Learning (RL) is successful in solving many complex Markov Decision Processes (MDPs) problems. However, agents often face unanticipated environmental changes aft…
Adversarial Neuron Pruning Purifies Backdoored Deep Models
Dongxian Wu, Yisen Wang
As deep neural networks (DNNs) are growing larger, their requirements for computational resources become huge, which makes outsourcing training more popular. Training in a third-pa…
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions
Pushi Zhang, Xiaoyu Chen, Li Zhao +3
A growing trend for value-based reinforcement learning (RL) algorithms is to capture more information than scalar value functions in the value network. One of the most well-known m…
Boost Neural Networks by Checkpoints
Feng Wang, Guoyizhe Wei, Qiao Liu +3
Training multiple deep neural networks (DNNs) and averaging their outputs is a simple way to improve the predictive performance. Nevertheless, the multiplied training cost prevents…
Stable Prediction on Graphs with Agnostic Distribution Shift
Shengyu Zhang, Kun Kuang, Jiezhong Qiu +5
Graph is a flexible and effective tool to represent complex structures in practice and graph neural networks (GNNs) have been shown to be effective on various graph tasks with rand…
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization
Fuchao Wei, Chenglong Bao, Yang Liu
Anderson mixing (AM) is an acceleration method for fixed-point iterations. Despite its success and wide usage in scientific computing, the convergence theory of AM remains unclear,…
Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality
Songyuan Zhang, Zhangjie Cao, Dorsa Sadigh +1
Most existing imitation learning approaches assume the demonstrations are drawn from experts who are optimal, but relaxing this assumption enables us to use a wider range of data.…
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks
Pengyong Li, Jun Wang, Ziliang Li +6
Self-supervised learning has gradually emerged as a powerful technique for graph representation learning. However, transferable, generalizable, and robust representation learning o…
Scale-Free Adversarial Multi-Armed Bandit with Arbitrary Feedback Delays
Jiatai Huang, Yan Dai, Longbo Huang
We consider the Scale-Free Adversarial Multi-Armed Bandit (MAB) problem with unrestricted feedback delays. In contrast to the standard assumption that all losses are -bounde…
MANDERA: Malicious Node Detection in Federated Learning via Ranking
Wanchuang Zhu, Benjamin Zi Hao Zhao, Simon Luo +2
Byzantine attacks hinder the deployment of federated learning algorithms. Although we know that the benign gradients and Byzantine attacked gradients are distributed differently, t…