Ruby Shrestha — accepted AI-conference papers

2026

ProReGen: Progressive Residual Generation under Attribute Correlations

Ruby Shrestha, Ajay Gopi, Casey Meisenzahl, Bipin Lekhak, Linwei Wang

ICLR 2026poster

Attribute correlations in the training data will compromise the ability of a deep generative model (DGM) to synthesize images with under-represented attribute combinations ($\textit{i.e.,}$ minority samples). Existing approaches mitigate this by data re-sampling to remove attribute correlations seen…

Cited by 0 Source ↗code ↗Scholar ↗

2024

DATS: Difficulty-Aware Task Sampler for Meta-Learning Physics-Informed Neural Networks

Maryam Toloubidokhti, Yubo Ye, Ryan Missel, Xiajun Jiang, Nilesh Kumar, Ruby Shrestha +1

ICLR 2024poster

Advancements in deep learning have led to the development of physics-informed neural networks (PINNs) for solving partial differential equations (PDEs) without being supervised by PDE solutions. While vanilla PINNs require training one network per PDE configuration, recent works have showed the pote…

Cited by 8 Source ↗PDF ↗Scholar ↗