106 citations · 565 across the 31 of their papers we have counts for
16 papers · 1 filter
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…
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…
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…
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…
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…
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…
A Meta Approach to Defend Noisy Labels by the Manifold Regularizer PSDR
Pengfei Chen, Benben Liao, Guangyong Chen +1
Noisy labels are ubiquitous in real-world datasets, which poses a challenge for robustly training deep neural networks (DNNs) since DNNs can easily overfit to the noisy labels. Mos…
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
Hongyao Tang, Jianye Hao, Guangyong Chen +4
Value functions are crucial for model-free Reinforcement Learning (RL) to obtain a policy implicitly or guide the policy updates. Value estimation heavily depends on the stochastic…
Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks
Guangyong Chen, Pengfei Chen, Yujun Shi +3
In this work, we propose a novel technique to boost training efficiency of a neural network. Our work is based on an excellent idea that whitening the inputs of neural networks can…
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Pengfei Chen, Benben Liao, Guangyong Chen +1
Noisy labels are ubiquitous in real-world datasets, which poses a challenge for robustly training deep neural networks (DNNs) as DNNs usually have the high capacity to memorize the…
Online Prediction of Dyadic Data with Heterogeneous Matrix Factorization
Guangyong Chen, Fengyuan Zhu, Pheng Ann Heng
Dyadic Data Prediction (DDP) is an important problem in many research areas. This paper develops a novel fully Bayesian nonparametric framework which integrates two popular and com…