activity
20202026
most citedAdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence

27 citations · 42 across the 8 of their papers we have counts for

collaborators

9 papers

astro-ph.GA2026

Decoupling candidate dual AGN from chance superpositions in the GOTHIC survey via a deep-learning framework

Bhavesh Mukheja, Snehanshu Saha, Anwesh Bhattacharya +3

Dual active galactic nuclei (DAGN) mark a critical phase in the evolution of merging galaxies and the pairing of supermassive black holes, yet they remain difficult to identify in…

astro-ph.GA2026

Investigating the Spectral Properties of Dual Nuclei in Galaxy Mergers from the GOTHIC survey: Supermassive Black Hole Growth, metal enrichment and Dual AGN

Prerana Biswas, Mousumi Das, Sudhanshu Barway +4

Dual nuclei systems are galaxy merger remnants or closely merging galaxies that have two distinct stellar cores separated by ~ 10pc to 10kpc. They are important laboratories for pr…

astro-ph.GA2025

Investigating the Bulge Morphology of Dual AGN Host Galaxies from the GOTHIC survey

C. P. Nehal, Mousumi Das, Sudhanshu Barway +4

We present a structural analysis of bulges in dual active galactic nuclei (AGN) host galaxies. Dual AGN arise in galaxy mergers where both supermassive black holes (SMBHs) are acti…

cs.CR2022★ 4 cited

Efficient ML Models for Practical Secure Inference

Vinod Ganesan, Anwesh Bhattacharya, Pratyush Kumar +3

ML-as-a-service continues to grow, and so does the need for very strong privacy guarantees. Secure inference has emerged as a potential solution, wherein cryptographic primitives a…

cs.LG2021

Encoding Involutory Invariances in Neural Networks

Anwesh Bhattacharya, Marios Mattheakis, Pavlos Protopapas

In certain situations, neural networks are trained upon data that obey underlying symmetries. However, the predictions do not respect the symmetries exactly unless embedded in the…

cs.LG2021

A Swarm Variant for the Schrödinger Solver

Urvil Nileshbhai Jivani, Omatharv Bharat Vaidya, Anwesh Bhattacharya +1

This paper introduces application of the Exponentially Averaged Momentum Particle Swarm Optimization (EM-PSO) as a derivative-free optimizer for Neural Networks. It adopts PSO's ma…

cs.NE2021★ 1 cited

Fairly Constricted Multi-Objective Particle Swarm Optimization

Anwesh Bhattacharya, Snehanshu Saha, Nithin Nagaraj

It has been well documented that the use of exponentially-averaged momentum (EM) in particle swarm optimization (PSO) is advantageous over the vanilla PSO algorithm. In the single-…

astro-ph.GA2020★ 10 cited

Automated Detection of Double Nuclei Galaxies using GOTHIC and the Discovery of a Large Sample of Dual AGN

Anwesh Bhattacharya, Nehal C. P., Mousumi Das +3

We present a novel algorithm to detect double nuclei galaxies (DNG) called GOTHIC (Graph BOosted iterated HIll Climbing) - that detects whether a given image of a galaxy has two or…

cs.NE2020★ 27 cited

AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence

Rohan Mohapatra, Snehanshu Saha, Carlos A. Coello Coello +3

This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks. In order to support…