Anirbit Mukherjee
Title
Cited by
Cited by
Year
Understanding deep neural networks with rectified linear units
R Arora, A Basu, P Mianjy, A Mukherjee
The International Conference on Learning Representations (ICLR) 2018, 2016
2512016
Convergence guarantees for RMSProp and ADAM in non-convex optimization and their comparison to Nesterov acceleration on autoencoders
S De, A Mukherjee, E Ullah
arXiv preprint arXiv:1807.06766, 2018
42*2018
Sparse coding and autoencoders
A Rangamani, A Mukherjee, A Basu, A Arora, T Ganapathi, S Chin, ...
2018 IEEE International Symposium on Information Theory (ISIT), 36-40, 2018
12*2018
Lower bounds over Boolean inputs for deep neural networks with ReLU gates
A Mukherjee, A Basu
arXiv preprint arXiv:1711.03073, 2017
42017
Guarantees on learning depth-2 neural networks under a data-poisoning attack
A Mukherjee, R Muthukumar
arXiv preprint arXiv:2005.01699, 2020
12020
A Study of the Mathematics of Deep Learning
A Mukherjee
Johns Hopkins University, 2020
2020
A Study of Neural Training with Non-Gradient and Noise Assisted Gradient Methods
A Mukherjee, R Muthukumar
arXiv preprint arXiv:2005.04211, 2020
2020
Improving PAC-Bayes bounds on risk of neural nets using geometrical properties of training
A Mukherjee, D Roy, P Rastogi, J Yang
ICML 2019 Workshop, Understanding and Improving Generalization in Deep Learning, 2019
2019
Renyi entropy of the critical O (N) model
A Mukherjee
arXiv preprint arXiv:1512.01226, 2015
2015
N-point correlations of dark matter tracers: Renormalization with univariate biasing and its O (f_ {NL}) terms with bivariate biasing
A Mukherjee
arXiv preprint arXiv:1307.7714, 2013
2013
The Landau-Ginzburg/Calabi-Yau Phase Transition (A Review)
Anirbit
http://guava.physics.uiuc.edu/~nigel/courses/563/Essays_2012/PDF/Mukherjee.pdf, 2012
2012
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Articles 1–11