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Found 8 papers, 2 papers with code
Reservoir Computing Benchmarks: a review, a taxonomy, some best practices
Reservoir Computing is an Unconventional Computation model to perform computation on various different substrates, such as RNNs or physical materials.
Unsupervised self-organising map of prostate cell Raman spectra shows disease-state subclustering
Prostate cancer is a disease which poses an interesting clinical question: should it be treated?
Optimising network interactions through device agnostic models
Physically implemented neural networks hold the potential to achieve the performance of deep learning models by exploiting the innate physical properties of devices as computational tools.
A perspective on physical reservoir computing with nanomagnetic devices
Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry.
Quantifying the Computational Capability of a Nanomagnetic Reservoir Computing Platform with Emergent Magnetization Dynamics
Devices based on arrays of interconnected magnetic nano-rings with emergent magnetization dynamics have recently been proposed for use in reservoir computing applications, but for them to be computationally useful it must be possible to optimise their dynamical responses.
Reservoir Computing with Magnetic Thin Films
Inspired by the nonlinear dynamics of neural networks, new unconventional computing hardware has emerged with the potential to exploit natural phenomena and gain efficiency, in a similar manner to biological systems.
MetaChem: An Algebraic Framework for Artificial Chemistries
We introduce MetaChem, a language for representing and implementing Artificial Chemistries.
Emerging Technologies
Evolving Graphs with Semantic Neutral Drift
We introduce the concept of Semantic Neutral Drift (SND) for genetic programming (GP), where we exploit equivalence laws to design semantics preserving mutations guaranteed to preserve individuals' fitness scores.
