Avideep Mukherjee
PhD Student
RM-504, Rajeev Motwani Building, IIT Kanpur, Kanpur-208016, India
avideep@cse.iitk.ac.in, avideep@iitk.ac.in, mukherjeeavideep@gmail.com
Research Interests
Machine Learning
Deep Generative Modelling
Computer Vision
Languages
Bengali
English
Hindi
About
I am a PhD Student in the Department of Computer Science and Engineering at IIT Kanpur advised by Prof. Piyush Rai and Prof. Vinay P. Namboodiri. My research interests include Machine Learning, Deep Learning, Deep Generative Modelling. I completed my Masters in Computer Science from Ramakrishna Mission Vivekananda Educational and Research Institute where I did my Masters Thesis under the supervision of Dr. Tanmay Basu. You can find my resume here.
Work Experience
Teaching Assistant / IIT Kanpur
August 2018 - July 2023
Senior Student Research Associate / IIT Kanpur
August 2022 - July 2024
Software Development Engineer Intern / LinkedIn Corporation
June 2021 - August 2021
Tutor / IIT Kanpur
August 2019 - August 2022
Summer Intern / ISI Kolkata
May 2017 - July 2017
Education
Indian Institute of Technology Kanpur
2018 - Present
PhD Student in the Department of Computer Science and Engineering
Advisor : Prof. Vinay P. Namboodiri and Prof. Piyush Rai
Ramakrishna Mission Vivekananda Educational and Research Institute
2016 - 2018
M.Sc. in Computer Science
Advisor : Dr. Tanmay Basu
Publication(s)
Mukherjee, A, Banerjee, S, Rai, P & Namboodiri, VP 2024, RISSOLE: Parameter-efficient Diffusion Models via Block-wise Generation and Retrieval-Guidance, in Proceedings of the 35th British Machine Vision Conference (BMVC 2024), Glasgow, UK, November 25-28, 2024.
Mukherjee, A, Patro, BN & Namboodiri, V 2023, Attentive Contractive Flow with Lipschitz Constrained Self-Attention, in 34th British Machine Vision Conference 2023, BMVC 2023, Aberdeen, UK, November 20-24, 2023. BMVA. [PDF] [CODE]
Pandey, K, Mukherjee, A, Rai, P & Kumar, A 2022, DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents, in Transactions on Machine Learning Research. [PDF] [CODE]
Pandey, K, Mukherjee, A, Rai, P & Kumar, A 2021, VAEs meet Diffusion Models: Efficient and High-Fidelity Generation, in NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications.[PDF] [CODE]
Banerjee, S, Verma, VK,Mukherjee, A, Gupta, D, Namboodiri, VP, & Rai, P 2024, Verse: Virtual-gradient Aware Streaming Lifelong Learning with Anytime Inference, in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2024.[PDF]
Tripathi, S, Jain, V, Madhwal,Mukherjee, A, S, Bergin, MH, Bhave, P, Carlson, D, Jiang, Z, & Rai, P, A Hybrid Approach for Integrating Micro-Satellite Images and Sensors Network-Based Ground Measurements Using Deep Learning for High-Resolution Prediction of Fine Particulate Matter (PM2.5) over an Indian City, Lucknow, Manuscript under review at Atmospheric Environment.
Tripathi, S, Jain, V,Mukherjee, A, Banerjee, S, Rai, P, & Madhwal, S 2023, Predicting PM2.5 based on micro-satellite imagery and low-cost sensor network using CNN-RT-RF Joint Model, No. EGU23-12426, Copernicus Meetings, 2023.[LINK]
Mukherjee, A & Basu, T 2018, A medoid-based weighting scheme for nearest-neighbor decision rule toward effective text categorization, in SN Applied Sciences 2, pp. 1-9.[PDF] [CODE]
Mukherjee, A & Basu, T 2018, An Effective Nearest Neighbor Classification Technique Using Medoid Based Weighting Scheme, in Robert Stahlbock, Gary M. Weiss, Mahmoud Abou-Nasr, Proceedings of the 2018 International Conference on Data Science, pp. 231-4.[PDF] [CODE]