38 citations · 120 across the 24 of their papers we have counts for
9 papers · 1 filter
Biological Factor Regulatory Neural Network
Xinnan Dai, Caihua Shan, Jie Zheng +2
Genes are fundamental for analyzing biological systems and many recent works proposed to utilize gene expression for various biological tasks by deep learning models. Despite their…
Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One
Yezhen Wang, Tong Che, Bo Li +4
Autoregressive generative models are commonly used, especially for those tasks involving sequential data. They have, however, been plagued by a slew of inherent flaws due to the in…
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Xiang Li, Renyu Zhu, Yao Cheng +4
We investigate graph neural networks on graphs with heterophily. Some existing methods amplify a node's neighborhood with multi-hop neighbors to include more nodes with homophily.…
Neural Piecewise-Constant Delay Differential Equations
Qunxi Zhu, Yifei Shen, Dongsheng Li +1
Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data…
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…
Energy-Based Open-World Uncertainty Modeling for Confidence Calibration
Yezhen Wang, Bo Li, Tong Che +3
Confidence calibration is of great importance to the reliability of decisions made by machine learning systems. However, discriminative classifiers based on deep neural networks ar…
Invariant Information Bottleneck for Domain Generalization
Bo Li, Yifei Shen, Yezhen Wang +6
Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain generalization. Nevertheless, the loss function is difficult to optimize for nonlinear…
Decentralized Statistical Inference with Unrolled Graph Neural Networks
He Wang, Yifei Shen, Ziyuan Wang +4
In this paper, we investigate the decentralized statistical inference problem, where a network of agents cooperatively recover a (structured) vector from private noisy samples with…
Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning
Yiqin Yu, Xu Min, Shiwan Zhao +5
In real world applications like healthcare, it is usually difficult to build a machine learning prediction model that works universally well across different institutions. At the s…