1 citations · 2 across the 10 of their papers we have counts for
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Learning Operators for Symmetry Discovery: From Lie Groups to Local Symmetry Geometry
Pavan Karjol, Scindhiya Laxmi, Prathosh AP
Continuous symmetry discovery is typically formulated as a separate estimation problem for each function or dataset. We instead investigate whether the map from a function to its s…
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…
AdaKD: Dynamic Knowledge Distillation of ASR models using Adaptive Loss Weighting
Shreyan Ganguly, Roshan Nayak, Rakshith Rao +2
Knowledge distillation, a widely used model compression technique, works on the basis of transferring knowledge from a cumbersome teacher model to a lightweight student model. The…
WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer
Kumar Shubham, Aishwarya Jayagopal, Syed Mohammed Danish +2
Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients. To advance research on personalized treatment strategies, th…
CoNO: Complex Neural Operator for Continuous Dynamical Systems
Karn Tiwari, N M Anoop Krishnan, Prathosh A P
Neural operators extend data-driven models to map between infinite-dimensional functional spaces. These models have successfully solved continuous dynamical systems represented by…
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…
Discovering mesoscopic descriptions of collective movement with neural stochastic modelling
Utkarsh Pratiush, Arshed Nabeel, Vishwesha Guttal +1
Collective motion is an ubiquitous phenomenon in nature, inspiring engineers, physicists and mathematicians to develop mathematical models and bio-inspired designs. Collective moti…
Adaptive Mixing of Auxiliary Losses in Supervised Learning
Durga Sivasubramanian, Ayush Maheshwari, Pradeep Shenoy +2
In several supervised learning scenarios, auxiliary losses are used in order to introduce additional information or constraints into the supervised learning objective. For instance…