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Found 5 papers, 1 papers with code
Evolving-to-Learn Reinforcement Learning Tasks with Spiking Neural Networks
This paper aims to make this process easier by employing an evolutionary algorithm that evolves suitable synaptic plasticity rules for the task at hand.
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy
In this work we approach, for the first time, the intensity reconstruction problem from a self-supervised learning perspective.
How Do Neural Networks Estimate Optical Flow? A Neuropsychology-Inspired Study
For our investigation, we focus on FlowNetS, as it is the prototype of an encoder-decoder neural network for optical flow estimation.
Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs
In light of this, we want to mimic flying insects in terms of their processing capabilities, and consequently show the efficiency of this approach in the real world.
Self-supervised learning: When is fusion of the primary and secondary sensor cue useful?
In this article, a theoretical analysis is performed on the fusion of the primary and secondary cue in a minimal model of SSL.
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