Arunselvan Ramaswamy | DeepAI

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Featured Co-authors

Shalabh Bhatnagar
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Daniel E. Quevedo
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Holger Karl
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Adrian Redder
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Sebastian Niehaus
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Artus Krohn-Grimberghe
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Burak Demirel
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Simon Malberg
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Martin Martens
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∙ 05/20/2023

A Framework for Provably Stable and Consistent Training of Deep Feedforward Networks

We present a novel algorithm for training deep neural networks in superv...

0 Arunselvan Ramaswamy, et al. ∙

∙ 05/11/2023

Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delays

We generalize the Borkar-Meyn stability Theorem (BMT) to distributed sto...

0 Adrian Redder, et al. ∙

∙ 01/27/2022

Distributed gradient-based optimization in the presence of dependent aperiodic communication

Iterative distributed optimization algorithms involve multiple agents th...

0 Adrian Redder, et al. ∙

∙ 01/03/2022

Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms

We present sufficient conditions that ensure convergence of the multi-ag...

0 Adrian Redder, et al. ∙

∙ 05/10/2021

Practical sufficient conditions for convergence of distributed optimisation algorithms over communication networks with interference

Information exchange over networks can be affected by various forms of d...

0 Adrian Redder, et al. ∙

∙ 08/25/2020

Theory of Deep Q-Learning: A Dynamical Systems Perspective

Deep Q-Learning is an important algorithm, used to solve sequential deci...

0 Arunselvan Ramaswamy, et al. ∙

∙ 05/15/2019

Deep reinforcement learning for scheduling in large-scale networked control systems

This work considers the problem of control and resource scheduling in ne...

0 Adrian Redder, et al. ∙

∙ 03/17/2019

DSPG: Decentralized Simultaneous Perturbations Gradient Descent Scheme

In this paper, we present an asynchronous approximate gradient method th...

0 Arunselvan Ramaswamy, et al. ∙

∙ 10/15/2018

Multi-Stage Reinforcement Learning For Object Detection

We present a reinforcement learning approach for detecting objects withi...

0 Jonas Koenig, et al. ∙

∙ 03/08/2018

DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling

We consider networked control systems consisting of multiple independent...

0 Burak Demirel, et al. ∙

∙ 02/22/2018

Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning

Asynchronous stochastic approximations are an important class of model-f...

0 Arunselvan Ramaswamy, et al. ∙

∙ 09/14/2017

Conditions for Stability and Convergence of Set-Valued Stochastic Approximations: Applications to Approximate Value and Fixed point Iterations

The main aim of this paper is the development of easily verifiable suffi...

0 Arunselvan Ramaswamy, et al. ∙

∙ 04/01/2016

Analysis of gradient descent methods with non-diminishing, bounded errors

The main aim of this paper is to provide an analysis of gradient descent...

0 Arunselvan Ramaswamy, et al. ∙

∙ 04/23/2015

Stability of Stochastic Approximations with `Controlled Markov' Noise and Temporal Difference Learning

In this paper we present a `stability theorem' for stochastic approximat...

0 Arunselvan Ramaswamy, et al. ∙

∙ 02/06/2015

Stochastic recursive inclusion in two timescales with an application to the Lagrangian dual problem

In this paper we present a framework to analyze the asymptotic behavior ...

0 Arunselvan Ramaswamy, et al. ∙

∙ 02/06/2015

A Generalization of the Borkar-Meyn Theorem for Stochastic Recursive Inclusions

In this paper the stability theorem of Borkar and Meyn is extended to in...

0 Arunselvan Ramaswamy, et al. ∙

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