no code implementations • 27 Nov 2023 • Sriram Anbalagan, Sai Shashank GP, Deepesh Agarwal, Balasubramaniam Natarajan, Babji Srinivasan
To overcome this limitation, we propose a foundational model-based Active Learning framework that utilizes less amount of labeled samples, which are most informative and harnesses a large amount of available unlabeled data by effectively combining Active Learning and Contrastive Self-Supervised Learning techniques.
no code implementations • 31 Jul 2023 • Sriram Anbalagan, Deepesh Agarwal, Balasubramaniam Natarajan, Babji Srinivasan
However, the data distribution can vary across different operating conditions during real-world operating scenarios of electrical motors.
no code implementations • 16 Jun 2022 • Sai Munikoti, Deepesh Agarwal, Laya Das, Mahantesh Halappanavar, Balasubramaniam Natarajan
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming.
no code implementations • 20 May 2022 • Sai Munikoti, Deepesh Agarwal, Laya Das, Balasubramaniam Natarajan
Graph Neural Networks (GNN) provide a powerful framework that elegantly integrates Graph theory with Machine learning for modeling and analysis of networked data.
no code implementations • 7 Oct 2021 • Deepesh Agarwal, Pravesh Srivastava, Sergio Martin-del-Campo, Balasubramaniam Natarajan, Babji Srinivasan
Inspired by these practical challenges, we present a hybrid query strategy-based AL framework that addresses three practical challenges simultaneously: cold-start, oracle uncertainty and performance evaluation of Active Learner in the absence of ground truth.