19 citations · 27 across the 11 of their papers we have counts for
4 papers · 1 filter
Constructing sensible baselines for Integrated Gradients
Jai Bardhan, Cyrin Neeraj, Mihir Rawat +1
Machine learning methods have seen a meteoric rise in their applications in the scientific community. However, little effort has been put into understanding these "black box" model…
Loss function to optimise signal significance in particle physics
Jai Bardhan, Cyrin Neeraj, Subhadip Mitra +1
We construct a surrogate loss to directly optimise the significance metric used in particle physics. We evaluate our loss function for a simple event classification task using a li…
ReMOVE: A Reference-free Metric for Object Erasure
Aditya Chandrasekar, Goirik Chakrabarty, Jai Bardhan +2
We introduce , a novel reference-free metric for assessing object erasure efficacy in diffusion-based image editing models post-generation. Unlike existing measure…
Unsupervised and lightly supervised learning in particle physics
Jai Bardhan, Tanumoy Mandal, Subhadip Mitra +2
We review the main applications of machine learning models that are not fully supervised in particle physics, i.e., clustering, anomaly detection, detector simulation, and unfoldin…