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Found 8 papers, 1 papers with code
Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input
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
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.
Risk of Phase Incoherence in Wide Area Control of Synchronous Power Networks
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
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
However, artificial neural networks are known to exhibit poor robustness in presence of input noise, where the sequential architecture of RNNs exacerbates the problem.
Distributed Map Classification using Local Observations
We consider the problem of classifying a map using a team of communicating robots.
A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning
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.
Multi-Agent Image Classification via Reinforcement Learning
We investigate a classification problem using multiple mobile agents capable of collecting (partial) pose-dependent observations of an unknown environment.
