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Found 6 papers, 0 papers with code
The race to robustness: exploiting fragile models for urban camouflage and the imperative for machine learning security
Adversarial Machine Learning (AML) represents the ability to disrupt Machine Learning (ML) algorithms through a range of methods that broadly exploit the architecture of deep learning optimisation.
Response Component Analysis for Sea State Estimation Using Artificial Neural Networks and Vessel Response Spectral Data
The use of the `ship as a wave buoy analogy' (SAWB) provides a novel means to estimate sea states, where relationships are established between causal wave properties and vessel motion response information.
Disturbances in Influence of a Shepherding Agent is More Impactful than Sensorial Noise During Swarm Guidance
We then systematically study the impact of sensorial and actuation noise on performance.
Towards Crossing the Reality Gap with Evolved Plastic Neurocontrollers
A critical issue in evolutionary robotics is the transfer of controllers learned in simulation to reality.
A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach
The simultaneous control of multiple coordinated robotic agents represents an elaborate problem.
Evolving Spiking Neural Networks for Nonlinear Control Problems
Spiking Neural Networks are powerful computational modelling tools that have attracted much interest because of the bioinspired modelling of synaptic interactions between neurons.
