Anirbit Mukherjee

According to our database, Anirbit Mukherjee authored at least 18 papers between 2017 and 2023.

Collaborative distances:

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

2023

Depth-2 neural networks under a data-poisoning attack.

Sayar Karmakar, Anirbit Mukherjee, Theodore Papamarkou

Neurocomputing, May, 2023

Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp.

Dibyakanti Kumar, Anirbit Mukherjee

CoRR, 2023

LIPEx - Locally Interpretable Probabilistic Explanations - To Look Beyond The True Class.

Hongbo Zhu, Angelo Cangelosi, Procheta Sen, Anirbit Mukherjee

CoRR, 2023

Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets.

Pulkit Gopalani, Samyak Jha, Anirbit Mukherjee

CoRR, 2023

Size Lowerbounds for Deep Operator Networks.

Anirbit Mukherjee, Amartya Roy

CoRR, 2023

2022

Provable training of a ReLU gate with an iterative non-gradient algorithm.

Sayar Karmakar, Anirbit Mukherjee

Neural Networks, 2022

Global Convergence of SGD On Two Layer Neural Nets.

Pulkit Gopalani, Anirbit Mukherjee

CoRR, 2022

Capacity Bounds for the DeepONet Method of Solving Differential Equations.

Pulkit Gopalani, Sayar Karmakar, Anirbit Mukherjee

CoRR, 2022

An Empirical Study of the Occurrence of Heavy-Tails in Training a ReLU Gate.

Sayar Karmakar, Anirbit Mukherjee

CoRR, 2022

2021

Investigating the locality of neural network training dynamics.

Soham Dan, Phanideep Gampa, Anirbit Mukherjee

CoRR, 2021

A Study of the Mathematics of Deep Learning.

Anirbit Mukherjee

CoRR, 2021

2020

A Study of Neural Training with Non-Gradient and Noise Assisted Gradient Methods.

Anirbit Mukherjee, Ramchandran Muthukumar

CoRR, 2020

Guarantees on learning depth-2 neural networks under a data-poisoning attack.

Anirbit Mukherjee, Ramchandran Muthukumar

CoRR, 2020

2018

Convergence guarantees for RMSProp and ADAM in non-convex optimization and their comparison to Nesterov acceleration on autoencoders.

Amitabh Basu, Soham De, Anirbit Mukherjee, Enayat Ullah

CoRR, 2018

Sparse Coding and Autoencoders.

Akshay Rangamani, Anirbit Mukherjee, Amitabh Basu, Ashish Arora, Tejaswini Ganapathi, Sang (Peter) Chin, Trac D. Tran

Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

2017

Lower bounds over Boolean inputs for deep neural networks with ReLU gates.

Anirbit Mukherjee, Amitabh Basu

Electron. Colloquium Comput. Complex., 2017

Understanding Deep Neural Networks with Rectified Linear Units.

Raman Arora, Amitabh Basu, Poorya Mianjy, Anirbit Mukherjee

Electron. Colloquium Comput. Complex., 2017

Critical Points Of An Autoencoder Can Provably Recover Sparsely Used Overcomplete Dictionaries.

Akshay Rangamani, Anirbit Mukherjee, Ashish Arora, Tejaswini Ganapathy, Amitabh Basu, Sang (Peter) Chin, Trac D. Tran

CoRR, 2017