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Journal Articles
- 2024
[j4]Pulkit Gopalani, Samyak Jha, Anirbit Mukherjee:
Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets. Trans. Mach. Learn. Res. 2024 (2024)- [j3]Anirbit Mukherjee, Amartya Roy:
Size Lowerbounds for Deep Operator Networks. Trans. Mach. Learn. Res. 2024 (2024) - 2023
- [j2]Sayar Karmakar
, Anirbit Mukherjee, Theodore Papamarkou:
Depth-2 neural networks under a data-poisoning attack. Neurocomputing 532: 56-66 (2023) - 2022
- [j1]Sayar Karmakar, Anirbit Mukherjee:
Provable training of a ReLU gate with an iterative non-gradient algorithm. Neural Networks 151: 264-275 (2022)
Conference and Workshop Papers
- 2018
- [c2]Raman Arora, Amitabh Basu, Poorya Mianjy, Anirbit Mukherjee:
Understanding Deep Neural Networks with Rectified Linear Units. ICLR (Poster) 2018 - [c1]Akshay Rangamani, Anirbit Mukherjee, Amitabh Basu, Ashish Arora, Tejaswini Ganapathi, Sang (Peter) Chin, Trac D. Tran:
Sparse Coding and Autoencoders. ISIT 2018: 36-40
Informal and Other Publications
- 2024
- [i19]Matteo Tucat, Anirbit Mukherjee:
Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks. CoRR abs/2404.08624 (2024) - [i18]Mohan Ren, Zhihao Fang, Keren Li, Anirbit Mukherjee:
Improving PINNs By Algebraic Inclusion of Boundary and Initial Conditions. CoRR abs/2407.20741 (2024) - 2023
- [i17]Anirbit Mukherjee, Amartya Roy:
Size Lowerbounds for Deep Operator Networks. CoRR abs/2308.06338 (2023) - [i16]Pulkit Gopalani, Samyak Jha, Anirbit Mukherjee:
Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets. CoRR abs/2309.09258 (2023) - [i15]Hongbo Zhu, Angelo Cangelosi, Procheta Sen, Anirbit Mukherjee:
LIPEx - Locally Interpretable Probabilistic Explanations - To Look Beyond The True Class. CoRR abs/2310.04856 (2023) - [i14]Dibyakanti Kumar, Anirbit Mukherjee:
Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp. CoRR abs/2310.05169 (2023) - 2022
- [i13]Sayar Karmakar, Anirbit Mukherjee:
An Empirical Study of the Occurrence of Heavy-Tails in Training a ReLU Gate. CoRR abs/2204.12554 (2022) - [i12]Pulkit Gopalani, Sayar Karmakar, Anirbit Mukherjee:
Capacity Bounds for the DeepONet Method of Solving Differential Equations. CoRR abs/2205.11359 (2022) - [i11]Pulkit Gopalani, Anirbit Mukherjee:
Global Convergence of SGD On Two Layer Neural Nets. CoRR abs/2210.11452 (2022) - 2021
- [i10]Anirbit Mukherjee:
A Study of the Mathematics of Deep Learning. CoRR abs/2104.14033 (2021) - [i9]Soham Dan, Phanideep Gampa, Anirbit Mukherjee:
Investigating the locality of neural network training dynamics. CoRR abs/2111.01166 (2021) - 2020
- [i8]Anirbit Mukherjee, Ramchandran Muthukumar:
Guarantees on learning depth-2 neural networks under a data-poisoning attack. CoRR abs/2005.01699 (2020) - [i7]Anirbit Mukherjee, Ramchandran Muthukumar:
A Study of Neural Training with Non-Gradient and Noise Assisted Gradient Methods. CoRR abs/2005.04211 (2020) - 2018
- [i6]Amitabh Basu, Soham De, Anirbit Mukherjee, Enayat Ullah:
Convergence guarantees for RMSProp and ADAM in non-convex optimization and their comparison to Nesterov acceleration on autoencoders. CoRR abs/1807.06766 (2018) - 2017
- [i5]Akshay Rangamani, Anirbit Mukherjee, Ashish Arora, Tejaswini Ganapathy, Amitabh Basu, Sang (Peter) Chin, Trac D. Tran:
Critical Points Of An Autoencoder Can Provably Recover Sparsely Used Overcomplete Dictionaries. CoRR abs/1708.03735 (2017) - [i4]Anirbit Mukherjee, Amitabh Basu:
Lower bounds over Boolean inputs for deep neural networks with ReLU gates. CoRR abs/1711.03073 (2017) - [i3]Raman Arora, Amitabh Basu, Poorya Mianjy, Anirbit Mukherjee:
Understanding Deep Neural Networks with Rectified Linear Units. Electron. Colloquium Comput. Complex. TR17 (2017) - [i2]Anirbit Mukherjee, Amitabh Basu:
Lower bounds over Boolean inputs for deep neural networks with ReLU gates. Electron. Colloquium Comput. Complex. TR17 (2017) - 2016
- [i1]Raman Arora, Amitabh Basu, Poorya Mianjy, Anirbit Mukherjee:
Understanding Deep Neural Networks with Rectified Linear Units. CoRR abs/1611.01491 (2016)
Coauthor Index
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