106 citations · 565 across the 31 of their papers we have counts for
31 papers
Dual Ensembled Multiagent Q-Learning with Hypernet Regularizer
Yaodong Yang, Guangyong Chen, Hongyao Tang +3
Overestimation in single-agent reinforcement learning has been extensively studied. In contrast, overestimation in the multiagent setting has received comparatively little attentio…
Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes
Donghao Zhou, Jialin Li, Jinpeng Li +7
Large-scale well-annotated datasets are of great importance for training an effective object detector. However, obtaining accurate bounding box annotations is laborious and demandi…
DPPMask: Masked Image Modeling with Determinantal Point Processes
Junde Xu, Zikai Lin, Donghao Zhou +5
Masked Image Modeling (MIM) has achieved impressive representative performance with the aim of reconstructing randomly masked images. Despite the empirical success, most previous w…
Traj-MAE: Masked Autoencoders for Trajectory Prediction
Hao Chen, Jiaze Wang, Kun Shao +5
Trajectory prediction has been a crucial task in building a reliable autonomous driving system by anticipating possible dangers. One key issue is to generate consistent trajectory…
PointPatchMix: Point Cloud Mixing with Patch Scoring
Yi Wang, Jiaze Wang, Jinpeng Li +4
Data augmentation is an effective regularization strategy for mitigating overfitting in deep neural networks, and it plays a crucial role in 3D vision tasks, where the point cloud…
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction
Bowen Wang, Chen Liang, Jiaze Wang +7
Attaining the equilibrium state of a catalyst-adsorbate system is key to fundamentally assessing its effective properties, such as adsorption energy. Machine learning methods with…
Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning
Yang Yu, Danruo Deng, Furui Liu +4
Semi-supervised learning (SSL) methods assume that labeled data, unlabeled data and test data are from the same distribution. Open-set semi-supervised learning (Open-set SSL) consi…
Uncertainty Estimation by Fisher Information-based Evidential Deep Learning
Danruo Deng, Guangyong Chen, Yang Yu +2
Uncertainty estimation is a key factor that makes deep learning reliable in practical applications. Recently proposed evidential neural networks explicitly account for different un…
RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction
Donghao Zhou, Chunbin Gu, Junde Xu +4
In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures. However, it is slow, expensive, and harmful to ce…
MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails
Kexin Chen, Guangyong Chen, Junyou Li +2
Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amoun…
Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations
Yang Yu, Zixu Zhao, Yueming Jin +3
Surgical scene segmentation is fundamentally crucial for prompting cognitive assistance in robotic surgery. However, pixel-wise annotating surgical video in a frame-by-frame manner…
LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction
Bowen Wang, Guibao Shen, Dong Li +7
Precise congestion prediction from a placement solution plays a crucial role in circuit placement. This work proposes the lattice hypergraph (LH-graph), a novel graph formulation f…
Acknowledging the Unknown for Multi-label Learning with Single Positive Labels
Donghao Zhou, Pengfei Chen, Qiong Wang +2
Due to the difficulty of collecting exhaustive multi-label annotations, multi-label datasets often contain partial labels. We consider an extreme of this weakly supervised learning…
Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning
Danruo Deng, Guangyong Chen, Jianye Hao +2
The backpropagation networks are notably susceptible to catastrophic forgetting, where networks tend to forget previously learned skills upon learning new ones. To address such the…
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
Weiwen Liu, Feng Liu, Ruiming Tang +3
Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders. In Interactive Recommender Systems (IRS),…
Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction
Hongyao Tang, Jianye Hao, Guangyong Chen +6
Value function is the central notion of Reinforcement Learning (RL). Value estimation, especially with function approximation, can be challenging since it involves the stochasticit…
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
Pengfei Chen, Junjie Ye, Guangyong Chen +2
Supervised learning under label noise has seen numerous advances recently, while existing theoretical findings and empirical results broadly build up on the class-conditional noise…
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
Pengfei Chen, Junjie Ye, Guangyong Chen +2
For multi-class classification under class-conditional label noise, we prove that the accuracy metric itself can be robust. We concretize this finding's inspiration in two essentia…
A Rotation-Invariant Framework for Deep Point Cloud Analysis
Xianzhi Li, Ruihui Li, Guangyong Chen +3
Recently, many deep neural networks were designed to process 3D point clouds, but a common drawback is that rotation invariance is not ensured, leading to poor generalization to ar…
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
Yaodong Yang, Jianye Hao, Guangyong Chen +5
Recently, deep multiagent reinforcement learning (MARL) has become a highly active research area as many real-world problems can be inherently viewed as multiagent systems. A parti…
Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning
Yaodong Yang, Jianye Hao, Ben Liao +4
In many real-world tasks, multiple agents must learn to coordinate with each other given their private observations and limited communication ability. Deep multiagent reinforcement…
Wasserstein Collaborative Filtering for Item Cold-start Recommendation
Yitong Meng, Guangyong Chen, Benben Liao +2
The item cold-start problem seriously limits the recommendation performance of Collaborative Filtering (CF) methods when new items have either none or very little interactions. To…
Spectral-based Graph Convolutional Network for Directed Graphs
Yi Ma, Jianye Hao, Yaodong Yang +3
Graph convolutional networks(GCNs) have become the most popular approaches for graph data in these days because of their powerful ability to extract features from graph. GCNs appro…
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh +9
We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science. As of June 20th 2019, the dataset comprises of…