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Albert Matveev

2 accepted papers

2025

Light-Weight Diffusion Multiplier and Uncertainty Quantification for Fourier Neural Operators

NeurIPS 2025spotlight

Operator learning is a powerful paradigm for solving partial differential equations, with Fourier Neural Operators serving as a widely adopted foundation. However, FNOs face significant scalability challenges due to overparameterization and offer no native uncertainty quantification -- a key require…

Cited by 0SourceScholar
2019

ABC: A Big CAD Model Dataset for Geometric Deep Learning

CVPR 2019poster

We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation…

Cited by 613PDFScholar