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09/17/2023
Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets
In this note, we demonstrate a first-of-its-kind provable convergence of...
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08/11/2023
Size Lowerbounds for Deep Operator Networks
Deep Operator Networks are an increasingly popular paradigm for solving ...
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10/20/2022
Global Convergence of SGD On Two Layer Neural Nets
In this note we demonstrate provable convergence of SGD to the global mi...
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05/23/2022
Capacity Bounds for the DeepONet Method of Solving Differential Equations
In recent times machine learning methods have made significant advances ...
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04/26/2022
An Empirical Study of the Occurrence of Heavy-Tails in Training a ReLU Gate
A particular direction of recent advance about stochastic deep-learning ...
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11/01/2021
Investigating the locality of neural network training dynamics
A fundamental quest in the theory of deep-learning is to understand the ...
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04/28/2021
A Study of the Mathematics of Deep Learning
"Deep Learning"/"Deep Neural Nets" is a technological marvel that is now...
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05/08/2020
A Study of Neural Training with Non-Gradient and Noise Assisted Gradient Methods
In this work we demonstrate provable guarantees on the training of depth...
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05/04/2020
Guarantees on learning depth-2 neural networks under a data-poisoning attack
In recent times many state-of-the-art machine learning models have been ...
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07/18/2018
Convergence guarantees for RMSProp and ADAM in non-convex optimization and their comparison to Nesterov acceleration on autoencoders
RMSProp and ADAM continue to be extremely popular algorithms for trainin...
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11/08/2017
Lower bounds over Boolean inputs for deep neural networks with ReLU gates
Motivated by the resurgence of neural networks in being able to solve co...
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08/12/2017
Sparse Coding and Autoencoders
In "Dictionary Learning" one tries to recover incoherent matrices A^* ∈R...
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11/04/2016
