Neha Mukund Kalibhat

According to our database, Neha Mukund Kalibhat authored at least 13 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations.

Neha Mukund Kalibhat, Warren Richard Morningstar, Alex Bijamov, Luyang Liu, Karan Singhal, Philip Andrew Mansfield

Proceedings of UniReps: the Second Edition of the Workshop on Unifying Representations in Neural Models, 2025

Understanding the Effect of using Semantically Meaningful Tokens for Visual Representation Learning.

Neha Mukund Kalibhat, Priyatham Kattakinda, Sumit Nawathe, Arman Zarei, Nikita Seleznev, Samuel Sharpe, Senthil Kumar, Soheil Feizi

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2025

2024

Interpreting Visual Representations and Mitigating their Failures.

Neha Mukund Kalibhat

PhD thesis, 2024

Understanding the Effect of using Semantically Meaningful Tokens for Visual Representation Learning.

Neha Mukund Kalibhat, Priyatham Kattakinda, Arman Zarei, Nikita Seleznev, Samuel Sharpe, Senthil Kumar, Soheil Feizi

CoRR, 2024

Augmentations vs Algorithms: What Works in Self-Supervised Learning.

Warren R. Morningstar, Alex Bijamov, Chris Duvarney, Luke Friedman, Neha Mukund Kalibhat, Luyang Liu, Philip Andrew Mansfield, Renan A. Rojas-Gomez, Karan Singhal, Bradley Green, Sushant Prakash

CoRR, 2024

Measuring Self-Supervised Representation Quality for Downstream Classification Using Discriminative Features.

Neha Mukund Kalibhat, Kanika Narang, Hamed Firooz, Maziar Sanjabi, Soheil Feizi

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Identifying Interpretable Subspaces in Image Representations.

Neha Mukund Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi

Proceedings of the International Conference on Machine Learning, 2023

Adapting Self-Supervised Representations to Multi-Domain Setups.

Neha Mukund Kalibhat, Samuel Sharpe, Jeremy Goodsitt, C. Bayan Bruss, Soheil Feizi

Proceedings of the 34th British Machine Vision Conference 2023, 2023

2022

Understanding Failure Modes of Self-Supervised Learning.

Neha Mukund Kalibhat, Kanika Narang, Liang Tan, Hamed Firooz, Maziar Sanjabi, Soheil Feizi

CoRR, 2022

2021

Understanding Overparameterization in Generative Adversarial Networks.

Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi

CoRR, 2021

Understanding Over-parameterization in Generative Adversarial Networks.

Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi

Proceedings of the 9th International Conference on Learning Representations, 2021

Winning Lottery Tickets in Deep Generative Models.

Neha Mukund Kalibhat, Yogesh Balaji, Soheil Feizi

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2017

Software Troubleshooting Using Machine Learning.

Neha Mukund Kalibhat, Shreya Varshini, Chid Kollengode, Dinkar Sitaram, Subramaniam Kalambur

Proceedings of the 24th IEEE International Conference on High Performance Computing Workshops, 2017