Deep Submodular Peripteral Networks

no code implementations • 13 Mar 2024 • Gantavya Bhatt, Arnav Das, Jeff Bilmes

In this paper, we introduce deep submodular peripteral networks (DSPNs), a novel parametric family of submodular functions, and methods for their training using a contrastive-learning inspired GPC-ready strategy to connect and then tackle both of the above challenges.

Active Learning Contrastive Learning +1

Accelerating Batch Active Learning Using Continual Learning Techniques

no code implementations • 10 May 2023 • Arnav Das, Gantavya Bhatt, Megh Bhalerao, Vianne Gao, Rui Yang, Jeff Bilmes

A major problem with Active Learning (AL) is high training costs since models are typically retrained from scratch after every query round.

Active Learning Continual Learning

EinsteinPy: A Community Python Package for General Relativity

1 code implementation • 22 May 2020 • Shreyas Bapat, Ritwik Saha, Bhavya Bhatt, Shilpi Jain, Akshita Jain, Sofía Ortín Vela, Priyanshu Khandelwal, Jyotirmaya Shivottam, Jialin Ma, Gim Seng Ng, Pratyush Kerhalkar, Hrishikesh Sudam Sarode, Rishi Sharma, Manvi Gupta, Divya Gupta, Tushar Tyagi, Tanmay Rustagi, Varun Singh, Saurabh Bansal, Naman Tayal, Abhijeet Manhas, Raphael Reyna, Gaurav Kumar, Govind Dixit, Ratin Kumar, Sashank Mishra, Alpesh Jamgade, Raahul Singh, Rohit Sanjay, Khalid Shaikh, Bhavam Vidyarthi, Shamanth R Nayak K, Vineet Gandham, Nimesh Vashistha, Arnav Das, Saurabh, Shreyas Kalvankar, Ganesh Tarone, Atul Mangat, Suyog Garg, Bibek Gautam, Sitara Srinivasan, Aayush Gautam, Swaastick Kumar Singh, Suyash Salampuria, Zac Yauney, Nihar Gupte, Gagan Shenoy, Micky Yun Chan

Python is a free, easy to use a high-level programming language which has seen a huge expansion in the number of its users and developers in recent years.

General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics 83-04

Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers

no code implementations • 20 Apr 2020 • Asad Khan, E. A. Huerta, Arnav Das

The spin distribution of binary black hole mergers contains key information concerning the formation channels of these objects, and the astrophysical environments where they form, evolve and coalesce.