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)

Understanding Deep Neural Networks with Rectified Linear Units. - Raman Arora; Amitabh Basu; Poorya Mianjy; Anirbit Mukherjee (Electron. Colloquium Comput. Complex., 2017)

Lower bounds over Boolean inputs for deep neural networks with ReLU gates. - Anirbit Mukherjee; Amitabh Basu (Electron. Colloquium Comput. Complex., 2017)

Sparse Coding and Autoencoders. - Akshay Rangamani; Anirbit Mukherjee; Amitabh Basu; Ashish Arora; Tejaswini Ganapathi; Sang (Peter) Chin; Trac D. Tran (ISIT, 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)

Guarantees on learning depth-2 neural networks under a data-poisoning attack. - Anirbit Mukherjee; Ramchandran Muthukumar (CoRR, 2020)

A Study of Neural Training with Non-Gradient and Noise Assisted Gradient Methods. - Anirbit Mukherjee; Ramchandran Muthukumar (CoRR, 2020)

A Study of the Mathematics of Deep Learning. - Anirbit Mukherjee (CoRR, 2021)

Investigating the locality of neural network training dynamics. - Soham Dan; Phanideep Gampa; Anirbit Mukherjee (CoRR, 2021)

An Empirical Study of the Occurrence of Heavy-Tails in Training a ReLU Gate. - Sayar Karmakar; Anirbit Mukherjee (CoRR, 2022)

Capacity Bounds for the DeepONet Method of Solving Differential Equations. - Pulkit Gopalani; Sayar Karmakar; Anirbit Mukherjee (CoRR, 2022)

Global Convergence of SGD On Two Layer Neural Nets. - Pulkit Gopalani; Anirbit Mukherjee (CoRR, 2022)

Provable training of a ReLU gate with an iterative non-gradient algorithm. - Sayar Karmakar; Anirbit Mukherjee (Neural Networks, 2022)

LIPEx - Locally Interpretable Probabilistic Explanations - To Look Beyond The True Class. - Hongbo Zhu; Angelo Cangelosi; Procheta Sen; Anirbit Mukherjee (CoRR, 2023)

Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp. - Dibyakanti Kumar; Anirbit Mukherjee (CoRR, 2023)

Depth-2 neural networks under a data-poisoning attack. - Sayar Karmakar; Anirbit Mukherjee; Theodore Papamarkou (Neurocomputing, 2023)

Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks. - Matteo Tucat; Anirbit Mukherjee (CoRR, 2024)

Improving PINNs By Algebraic Inclusion of Boundary and Initial Conditions. - Mohan Ren; Zhihao Fang; Keren Li; Anirbit Mukherjee (CoRR, 2024)

Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets. - Pulkit Gopalani; Samyak Jha; Anirbit Mukherjee (Trans. Mach. Learn. Res., 2024)

Towards Size-Independent Generalization Bounds for Deep Operator Nets. - Pulkit Gopalani; Sayar Karmakar; Dibyakanti Kumar; Anirbit Mukherjee (Trans. Mach. Learn. Res., 2024)

Size Lowerbounds for Deep Operator Networks. - Anirbit Mukherjee; Amartya Roy (Trans. Mach. Learn. Res., 2024)

Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks. - Matteo Tucat; Anirbit Mukherjee; Procheta Sen; Mingfei Sun 001; Omar Rivasplata (Trans. Mach. Learn. Res., 2025)

Langevin Monte-Carlo Provably Learns Depth Two Neural Nets at Any Size and Data. - Dibyakanti Kumar; Samyak Jha; Anirbit Mukherjee (CoRR, 2025)

Noisy PDE Training Requires Bigger PINNs. - Sebastien Andre-Sloan; Anirbit Mukherjee; Matthew Colbrook (CoRR, 2025)