78 citations · 196 across the 15 of their papers we have counts for
6 papers · 1 filter
PGrad: Learning Principal Gradients For Domain Generalization
Zhe Wang, Jake Grigsby, Yanjun Qi
Machine learning models fail to perform when facing out-of-distribution (OOD) domains, a challenging task known as domain generalization (DG). In this work, we develop a novel DG t…
Online Adversarial Knowledge Distillation for Graph Neural Networks
Can Wang, Zhe Wang, Defang Chen +3
Knowledge distillation, a technique recently gaining popularity for enhancing model generalization in Convolutional Neural Networks (CNNs), operates under the assumption that both…
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning
Zhe Wang, Jake Grigsby, Arshdeep Sekhon +1
Optimization-based meta-learning typically assumes tasks are sampled from a single distribution - an assumption oversimplifies and limits the diversity of tasks that meta-learning…
Long-Range Transformers for Dynamic Spatiotemporal Forecasting
Jake Grigsby, Zhe Wang, Nam Nguyen +1
Multivariate time series forecasting focuses on predicting future values based on historical context. State-of-the-art sequence-to-sequence models rely on neural attention between…
Reconstructing a dynamical system and forecasting time series by self-consistent deep learning
Zhe Wang, Claude Guet
We introduce a self-consistent deep-learning framework which, for a noisy deterministic time series, provides unsupervised filtering, state-space reconstruction, identification of…
Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge
Arshdeep Sekhon, Zhe Wang, Yanjun Qi
Learning the differential statistical dependency network between two contexts is essential for many real-life applications, mostly in the high dimensional low sample regime. In thi…