Search Results for author: Nader Motee

Found 8 papers, 1 papers with code

Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input

no code implementations10 Mar 2022 Guangyi Liu, Arash Amini, Martin Takac, Nader Motee

For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices.


Emergence of Cascading Risk and Role of Spatial Locations of Collisions in Time-Delayed Platoon of Vehicles

no code implementations29 Dec 2021 Guangyi Liu, Christoforos Somarakis, Nader Motee

We develop a framework to assess the risk of cascading collisions in a platoon of vehicles in the presence of exogenous noise and communication time-delay.

Autonomous Vehicles

Risk of Phase Incoherence in Wide Area Control of Synchronous Power Networks

no code implementations29 Dec 2021 Christoforos Somarakis, Guangyi Liu, Nader Motee

We develop a framework to quantify systemic risk measures in a class of Wide-Area-Control (WAC) laws in power networks in the presence of noisy and time-delayed sensory data.

Risk of Cascading Failures in Time-Delayed Vehicle Platooning

no code implementations5 Sep 2021 Guangyi Liu, Christoforos Somarakis, Nader Motee

We develop a systemic risk framework to explore cascading systemic failures in networked control systems.

Robust Learning of Recurrent Neural Networks in Presence of Exogenous Noise

no code implementations3 May 2021 Arash Amini, Guangyi Liu, Nader Motee

However, artificial neural networks are known to exhibit poor robustness in presence of input noise, where the sequential architecture of RNNs exacerbates the problem.

A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning

no code implementations20 Sep 2019 Hossein K. Mousavi, Guangyi Liu, Weihang Yuan, Martin Takáč, Héctor Muñoz-Avila, Nader Motee

We propose a planning and perception mechanism for a robot (agent), that can only observe the underlying environment partially, in order to solve an image classification problem.

General Classification Image Classification +1

Multi-Agent Image Classification via Reinforcement Learning

1 code implementation13 May 2019 Hossein K. Mousavi, MohammadReza Nazari, Martin Takáč, Nader Motee

We investigate a classification problem using multiple mobile agents capable of collecting (partial) pose-dependent observations of an unknown environment.

Classification General Classification +2

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