Guangyong Chen

research

∙ 08/23/2023

Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes

Large-scale well-annotated datasets are of great importance for training...

0 Donghao Zhou, et al. ∙

research

∙ 03/21/2023

Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning

Semi-supervised learning (SSL) methods assume that labeled data, unlabel...

0 Yang Yu, et al. ∙

research

∙ 03/12/2023

Traj-MAE: Masked Autoencoders for Trajectory Prediction

Trajectory prediction has been a crucial task in building a reliable aut...

0 Hao Chen, et al. ∙

research

∙ 03/12/2023

PointPatchMix: Point Cloud Mixing with Patch Scoring

Data augmentation is an effective regularization strategy for mitigating...

0 Yi Wang, et al. ∙

research

∙ 03/06/2023

DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction

Attaining the equilibrium state of a catalyst-adsorbate system is key to...

0 Bowen Wang, et al. ∙

research

∙ 03/03/2023

Uncertainty Estimation by Fisher Information-based Evidential Deep Learning

Uncertainty estimation is a key factor that makes deep learning reliable...

0 Danruo Deng, et al. ∙

research

∙ 12/20/2022

RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction

In subcellular biological research, fluorescence staining is a key techn...

0 Donghao Zhou, et al. ∙

research

∙ 09/23/2022

Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation

Session-based recommendation (SBR) systems aim to utilize the user's sho...

0 Zhongwei Wan, et al. ∙

research

∙ 09/15/2022

Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction

Graph neural networks (GNNs) have drawn more and more attention from mat...

0 Chen Liang, et al. ∙

research

∙ 08/22/2022

MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails

Artificial intelligence has deeply revolutionized the field of medicinal...

0 Kexin Chen, et al. ∙

research

∙ 07/20/2022

Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations

Surgical scene segmentation is fundamentally crucial for prompting cogni...

4 Yang Yu, et al. ∙

research

∙ 04/12/2022

Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation

Sign language recognition and translation first uses a recognition modul...

4 Yong Cao, et al. ∙

research

∙ 03/30/2022

Acknowledging the Unknown for Multi-label Learning with Single Positive Labels

Due to the difficulty of collecting exhaustive multi-label annotations, ...

0 Donghao Zhou, et al. ∙

research

∙ 03/24/2022

LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction

Precise congestion prediction from a placement solution plays a crucial ...

0 Bowen Wang, et al. ∙

research

∙ 10/09/2021

Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning

The backpropagation networks are notably susceptible to catastrophic for...

0 Danruo Deng, et al. ∙

research

∙ 06/25/2021

Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning

Fairness in recommendation has attracted increasing attention due to bia...

0 Weiwen Liu, et al. ∙

research

∙ 12/10/2020

Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise

Supervised learning under label noise has seen numerous advances recentl...

0 Pengfei Chen, et al. ∙

research

∙ 12/08/2020

Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels

For multi-class classification under class-conditional label noise, we p...

0 Pengfei Chen, et al. ∙

research

∙ 03/16/2020

A Rotation-Invariant Framework for Deep Point Cloud Analysis

Recently, many deep neural networks were designed to process 3D point cl...

15 Xianzhi Li, et al. ∙

research

∙ 02/10/2020

Q-value Path Decomposition for Deep Multiagent Reinforcement Learning

Recently, deep multiagent reinforcement learning (MARL) has become a hig...

0 Yaodong Yang, et al. ∙

research

∙ 02/10/2020

Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning

In many real-world settings, a team of cooperative agents must learn to ...

0 Yaodong Yang, et al. ∙

research

∙ 09/10/2019

Wasserstein Collaborative Filtering for Item Cold-start Recommendation

The item cold-start problem seriously limits the recommendation performa...

0 Yitong Meng, et al. ∙

research

∙ 09/10/2019

PMD: A New User Distance for Recommender Systems

Collaborative filtering, a widely-used recommendation technique, predict...

0 Yitong Meng, et al. ∙

research

∙ 07/21/2019

Spectral-based Graph Convolutional Network for Directed Graphs

Graph convolutional networks(GCNs) have become the most popular approach...

0 Yi Ma, et al. ∙

research

∙ 06/22/2019

Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models

We introduce a new molecular dataset, named Alchemy, for developing mach...

15 Guangyong Chen, et al. ∙

research

∙ 06/13/2019

A Meta Approach to Defend Noisy Labels by the Manifold Regularizer PSDR

Noisy labels are ubiquitous in real-world datasets, which poses a challe...

0 Pengfei Chen, et al. ∙

research

∙ 06/13/2019

Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization

Graph Neural Networks (GNNs) achieve an impressive performance on struct...

0 Pengfei Chen, et al. ∙

research

∙ 06/06/2019

Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric

The vulnerability to slight input perturbations is a worrying yet intrig...

0 Yujun Shi, et al. ∙

research

∙ 05/27/2019

Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction

Value functions are crucial for model-free Reinforcement Learning (RL) t...

0 Hongyao Tang, et al. ∙

research

∙ 05/15/2019

Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks

In this work, we propose a novel technique to boost training efficiency ...

0 Guangyong Chen, et al. ∙

research

∙ 05/13/2019

Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

Noisy labels are ubiquitous in real-world datasets, which poses a challe...

0 Pengfei Chen, et al. ∙

research

∙ 01/13/2016

Online Prediction of Dyadic Data with Heterogeneous Matrix Factorization

Dyadic Data Prediction (DDP) is an important problem in many research ar...

0 Guangyong Chen, et al. ∙

research

∙ 01/13/2016

Blind Image Denoising via Dependent Dirichlet Process Tree

Most existing image denoising approaches assumed the noise to be homogen...

0 Fengyuan Zhu, et al. ∙

Guangyong Chen

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