7 citations · 9 across the 6 of their papers we have counts for
6 papers
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…
A survey of deep learning optimizers -- first and second order methods
Rohan Kashyap
Deep Learning optimization involves minimizing a high-dimensional loss function in the weight space which is often perceived as difficult due to its inherent difficulties such as s…
GPT-Neo for commonsense reasoning -- a theoretical and practical lens
Rohan Kashyap, Vivek Kashyap, Narendra C. P.
Recent work has demonstrated substantial gains in pre-training large-language models (LLMs) followed by supervised fine-tuning on the downstream task. In this paper, we evaluate th…