Sourav Pal

I am a third year PhD student of Computer Sciences at UW-Madison. I am very fortunate to be advised by Prof. Vikas Singh. My broad research interests are in Machine Learning and their applications to problems in Computer Vision. I am specifically interested in harmonic analysis and hybrid models which combine strong function approximation properties of neural networks along side differntial equations to model real life physical processes.

Before coming to Madison I was part of Adobe Acrobat Reader team. Even before I was a Research Intern at the BigData Experience Lab of Adobe Research where I worked with an amazing mentor, Dr. Ritwik Sinha.

As an undergrad I spent four beautiful years, at the Indian Institute of Technology (IIT) Kharagpur from where I graduated with a B.Tech (Hons.) in Computer Science and Engineering. I was fortunate enough to be advised by Prof. Pabitra Mitra during my undergraduate studies.

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Recent Updates:

May 22: Our paper on multi resolution analysis for efficient self attention got accepted to ICML 2022

Mar 22: Our paper on machine unlearning got accepted to CVPR 2022

Publications
Multi Resolution Analysis (MRA) for Approximate Self-Attention
Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn Fung, Vikas Singh
To appear ICML, 2022

Details to follow!

Deep Unlearning via Randomized Conditionally Independent Hessians
Ronak Mehta*, Sourav Pal*, Vikas Singh, Sathya N. Ravi
To appear CVPR, 2022

Details to follow!

D-FJ: Deep Neural Network Based Factuality Judgment
Ankan Mullick, Sourav Pal, Projjal Chanda, Arijit Panigrahy, Anurag Bharadwaj, Siddhant Singh, Tanmoy Dam
TrueFact, SIGKDD, 2019

Deep neural networks to detect facts and opinions from online news media. We have also shown how factuality, opinionatedness and sentiment fraction of different news articles changes over certain events in different time frames.

Visual Attention for Behavioral Cloning in Autonomous Driving
Sourav Pal*, Tharun Mohandoss*, Pabitra Mitra
ICMV, 2018

We present two methods of predicting visual attention maps. The first method is a supervised learning approach in which we collect eye-gaze data for the task of driving and use this to train a model for predicting the attention map. The second method is a novel unsupervised approach where we train a model to learn to predict attention as it learns to drive a car.

Saliency Prediction for Mobile User Interfaces
Prakhar Gupta, Sourav Pal*, Shubh Gupta* , Ajaykrishnan Jayagopal*, Ritwik Sinha
WACV, 2018

We introduce deep learning models for saliency prediction for mobile user interfaces at the element level to improve their usability.

Patents
Saliency Prediction for a Mobile User Interface

Saliency Prediction for Informational Documents

Stack Overflow'ed from here