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A Neurosymbolic Approach to the Verification of Temporal Logic Properties of Learning enabled Control Systems
In this paper, we present a model for the verification of Neural Network (NN) controllers for general STL specifications using a custom neural architecture where we map an STL formula into a feed-forward neural network with ReLU activation.
Ultrafast single-channel machine vision based on neuro-inspired photonic computing
This approach allows for compressive acquisitions of visual information with a single channel at gigahertz rates, outperforming conventional approaches, and enables its direct photonic processing using a photonic reservoir computer in a time domain.
Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectives
In this paper, we consider the problem of synthesizing a controller in the presence of uncertainty such that the resulting closed-loop system satisfies certain hard constraints while optimizing certain (soft) performance objectives.
Risk-Bounded Control with Kalman Filtering and Stochastic Barrier Functions
Our goal is to design controllers that bound the probability of a system failure in finite-time to a given desired value.
Reachability Analysis of Convolutional Neural Networks
Besides the computation of reachable sets, our approach is also capable of backtracking to the input domain given an output reachable set.
