4 papers
Fair Dataset Distillation via Cross-Group Barycenter Alignment
Mohammad Hossein Moslemi, Nima Hosseini Dashtbayaz, Zhimin Mei +2
Dataset Distillation aims to compress a large dataset into a small synthetic one while maintaining predictive performance. We show that as different demographic groups exhibit dist…
Physics-informed Reduced Order Modeling of Time-dependent PDEs via Differentiable Solvers
Nima Hosseini Dashtbayaz, Hesam Salehipour, Adrian Butscher +1
Reduced-order modeling (ROM) of time-dependent and parameterized differential equations aims to accelerate the simulation of complex high-dimensional systems by learning a compact…
On the Benefits of Attribute-Driven Graph Domain Adaptation
Ruiyi Fang, Bingheng Li, Zhao Kang +5
Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets. Rece…
Physics-Informed Neural Networks: Minimizing Residual Loss with Wide Networks and Effective Activations
Nima Hosseini Dashtbayaz, Ghazal Farhani, Boyu Wang +1
The residual loss in Physics-Informed Neural Networks (PINNs) alters the simple recursive relation of layers in a feed-forward neural network by applying a differential operator, r…