106 citations · 565 across the 31 of their papers we have counts for
8 papers · 1 filter
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…
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…
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric
Yujun Shi, Benben Liao, Guangyong Chen +3
The vulnerability to slight input perturbations is a worrying yet intriguing property of deep neural networks (DNNs). Despite many previous works studying the reason behind such ad…
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…