2 citations · 5 across the 15 of their papers we have counts for
5 papers · 1 filter
f-INE: A Hypothesis Testing Framework for Estimating Influence under Training Randomness
Subhodip Panda, Dhruv Tarsadiya, Shashwat Sourav +2
Influence estimation methods promise to explain and debug machine learning by estimating the impact of individual samples on the final model. Yet, existing methods collapse under t…
The Platonic Universe: Do Foundation Models See the Same Sky?
UniverseTBD, :, Trinidad Borrell +11
We investigate when foundation models converge towards shared representations, and how this convergence depends on model capacity, training regime, and model architecture. We take…
Cosmology-informed Neural Networks to infer dark energy equation-of-state
Anshul Verma, Shashwat Sourav, Pavan K. Aluri +1
We present a framework that combines physics-informed neural networks (PINNs) with Markov Chain Monte Carlo (MCMC) inference to constrain dynamical dark energy models using the Pan…
Covariant Energy Density Functionals for Neutron Star Matter Equation of State Modeling: Cross-Comparison Analysis Using \texttt{CompactObject}
João Cartaxo, Chun Huang, Tuhin Malik +5
This study analyzes and contrasts different phenomenological methods used to model the nuclear equation of state (EOS) for neutron star matter based on covariant energy density fun…
A Survey on Hypothesis Generation for Scientific Discovery in the Era of Large Language Models
Atilla Kaan Alkan, Shashwat Sourav, Maja Jablonska +14
Hypothesis generation is a fundamental step in scientific discovery, yet it is increasingly challenged by information overload and disciplinary fragmentation. Recent advances in La…