6 papers · 1 filter
Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise
Kumar Shubham, Pavan Karjol, Kiran M K +1
The performance of machine learning models often relies on large labeled datasets; however, data collected from diverse sources can contain label noise. Recent work has shown that,…
Spectral Discovery of Continuous Symmetries via Generalized Fourier Transforms
Pavan Karjol, Kumar Shubham, Prathosh AP
Continuous symmetries are fundamental to many scientific and learning problems, yet they are often unknown a priori. Existing symmetry discovery approaches typically search directl…
Learning Equivariant Functions via Quadratic Forms
Pavan Karjol, Vivek V Kashyap, Rohan Kashyap +1
In this study, we introduce a method for learning group (known or unknown) equivariant functions by learning the associated quadratic form corresponding to the group from…
Interpretable Discovery of One-parameter Subgroups: A Modular Framework for Elliptical, Hyperbolic, and Parabolic Symmetries
Pavan Karjol, Vivek V Kashyap, Rohan Kashyap +1
We propose a modular, data-driven framework for jointly learning unknown functional mappings and discovering the underlying one-parameter symmetry subgroup governing the data. Unli…
A Unified Framework for Discovering Discrete Symmetries
Pavan Karjol, Rohan Kashyap, Aditya Gopalan +1
We consider the problem of learning a function respecting a symmetry from among a class of symmetries. We develop a unified framework that enables symmetry discovery across a broad…
Neural Discovery of Permutation Subgroups
Pavan Karjol, Rohan Kashyap, Prathosh A P
We consider the problem of discovering subgroup of permutation group . Unlike the traditional -invariant networks wherein is assumed to be known, we present a met…