98 citations · 261 across the 12 of their papers we have counts for
12 papers
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
Shuxin Zheng, Jiyan He, Chang Liu +15
Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not fun…
Direct Molecular Conformation Generation
Jinhua Zhu, Yingce Xia, Chang Liu +9
Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology. Previou…
Towards Generating Real-World Time Series Data
Hengzhi Pei, Kan Ren, Yuqing Yang +3
Time series data generation has drawn increasing attention in recent years. Several generative adversarial network (GAN) based methods have been proposed to tackle the problem usua…
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning
Jongjin Park, Younggyo Seo, Chang Liu +4
Behavioral cloning has proven to be effective for learning sequential decision-making policies from expert demonstrations. However, behavioral cloning often suffers from the causal…
On the Generative Utility of Cyclic Conditionals
Chang Liu, Haoyue Tang, Tao Qin +2
We study whether and how can we model a joint distribution using two conditional models and that form a cycle. This is motivated by the observation that…
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior
Sang-gil Lee, Heeseung Kim, Chaehun Shin +7
Denoising diffusion probabilistic models have been recently proposed to generate high-quality samples by estimating the gradient of the data density. The framework defines the prio…
Learning Invariant Representations across Domains and Tasks
Jindong Wang, Wenjie Feng, Chang Liu +5
Being expensive and time-consuming to collect massive COVID-19 image samples to train deep classification models, transfer learning is a promising approach by transferring knowledg…
Generalizing to Unseen Domains: A Survey on Domain Generalization
Jindong Wang, Cuiling Lan, Chang Liu +6
Machine learning systems generally assume that the training and testing distributions are the same. To this end, a key requirement is to develop models that can generalize to unsee…
Latent Causal Invariant Model
Xinwei Sun, Botong Wu, Xiangyu Zheng +4
Current supervised learning can learn spurious correlation during the data-fitting process, imposing issues regarding interpretability, out-of-distribution (OOD) generalization, an…
Learning Causal Semantic Representation for Out-of-Distribution Prediction
Chang Liu, Xinwei Sun, Jindong Wang +5
Conventional supervised learning methods, especially deep ones, are found to be sensitive to out-of-distribution (OOD) examples, largely because the learned representation mixes th…
Learning to Match Distributions for Domain Adaptation
Chaohui Yu, Jindong Wang, Chang Liu +5
When the training and test data are from different distributions, domain adaptation is needed to reduce dataset bias to improve the model's generalization ability. Since it is diff…
Modeling Lost Information in Lossy Image Compression
Yaolong Wang, Mingqing Xiao, Chang Liu +2
Lossy image compression is one of the most commonly used operators for digital images. Most recently proposed deep-learning-based image compression methods leverage the auto-encode…