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Found 9 papers, 0 papers with code
Random pattern and frequency generation using a photonic reservoir computer with output feedback
We demonstrate the first opto-electronic reservoir computer with output feedback and test it on two examples of time series generation tasks: frequency and random pattern generation.
A Differentiable Physics Engine for Deep Learning in Robotics
Currently, robots are often treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent.
Embodiment of Learning in Electro-Optical Signal Processors
Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing.
Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalisation
Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals.
Towards Trainable Media: Using Waves for Neural Network-Style Training
In this paper we study the concept of using the interaction between waves and a trainable medium in order to construct a matrix-vector multiplier.
Photonic Delay Systems as Machine Learning Implementations
We perform physical experiments that demonstrate that the obtained input encodings work well in reality, and we show that optimized systems perform significantly better than the common Reservoir Computing approach.
Trainable and Dynamic Computing: Error Backpropagation through Physical Media
Machine learning algorithms, and more in particular neural networks, arguably experience a revolution in terms of performance.
Memristor models for machine learning
In this work, we explore the use of memristor networks for analog approximate computation, based on a machine learning framework called reservoir computing.
Training and Analysing Deep Recurrent Neural Networks
In this pa- per we study the effect of a hierarchy of recurrent neural networks on processing time series.
