Vivak Patel | DeepAI

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Featured Co-authors

Daniel Adrian Maldonado
4 publications
Shushu Zhang
3 publications
Bowen Tian
3 publications
Mohammad Jahangoshahi
2 publications
Nathaniel Pritchard
2 publications
Christian Varner
1 publication

∙ 09/19/2023

A Novel Gradient Methodology with Economical Objective Function Evaluations for Data Science Applications

Gradient methods are experiencing a growth in methodological and theoret...

0 Christian Varner, et al. ∙

∙ 08/09/2022

Towards Practical Large-scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification

As the scale of problems and data used for experimental design, signal p...

0 Nathaniel Pritchard, et al. ∙

∙ 04/04/2022

Randomized Block Adaptive Linear System Solvers

Randomized linear solvers leverage randomization to structure-blindly co...

0 Vivak Patel, et al. ∙

∙ 01/15/2022

Residual Tracking and Stopping for Iterative Random Sketching

Iterative random sketching (IRS) offers a computationally expedient appr...

0 Nathaniel Pritchard, et al. ∙

∙ 10/04/2021

Global Convergence and Stability of Stochastic Gradient Descent

In machine learning, stochastic gradient descent (SGD) is widely deploye...

0 Vivak Patel, et al. ∙

∙ 04/10/2021

Convergence of Adaptive, Randomized, Iterative Linear Solvers

Deterministic and randomized, row-action and column-action linear solver...

0 Vivak Patel, et al. ∙

∙ 11/05/2020

Stochastic Approximation for High-frequency Observations in Data Assimilation

With the increasing penetration of high-frequency sensors across a numbe...

0 Shushu Zhang, et al. ∙

∙ 04/01/2020

Stopping Criteria for, and Strong Convergence of, Stochastic Gradient Descent on Bottou-Curtis-Nocedal Functions

While Stochastic Gradient Descent (SGD) is a rather efficient algorithm ...

0 Vivak Patel, et al. ∙

∙ 09/14/2017

The Impact of Local Geometry and Batch Size on the Convergence and Divergence of Stochastic Gradient Descent

Stochastic small-batch (SB) methods, such as mini-batch Stochastic Gradi...

0 Vivak Patel, et al. ∙

∙ 02/01/2017

On SGD's Failure in Practice: Characterizing and Overcoming Stalling

Stochastic Gradient Descent (SGD) is widely used in machine learning pro...

0 Vivak Patel, et al. ∙

∙ 12/03/2015

Kalman-based Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning

Modern proximal and stochastic gradient descent (SGD) methods are believ...

0 Vivak Patel, et al. ∙

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