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Found 15 papers, 5 papers with code
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.
Machine learning using magnetic stochastic synapses
For single measurements, the rule results in binary synapses with minimal stochasticity, sacrificing potential performance for robustness.
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.
EchoVPR: Echo State Networks for Visual Place Recognition
We report that the addition of ESNs to pre-processed convolutional neural networks led to a dramatic boost in performance in comparison to non-recurrent networks in five out of six standard benchmarks (GardensPoint, SPEDTest, ESSEX3IN1, Oxford RobotCar, and Nordland), demonstrating that ESNs are able to capture the temporal structure inherent in VPR problems.
A Robotic Model of Hippocampal Reverse Replay for Reinforcement Learning
Hippocampal reverse replay is thought to contribute to learning, and particularly reinforcement learning, in animals.
Exploiting Multiple Timescales in Hierarchical Echo State Networks
Echo state networks (ESNs) are a powerful form of reservoir computing that only require training of linear output weights whilst the internal reservoir is formed of fixed randomly connected neurons.
Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing
Machine learning, particularly in the form of deep learning, has driven most of the recent fundamental developments in artificial intelligence.
A semi-supervised sparse K-Means algorithm
We consider the problem of data clustering with unidentified feature quality and when a small amount of labelled data is provided.
SpaRCe: Improved Learning of Reservoir Computing Systems through Sparse Representations
The read-out weights and the thresholds are learned by an on-line gradient rule that minimises an error function on the outputs of the network.
An empirical comparison between stochastic and deterministic centroid initialisation for K-Means variations
K-Means is one of the most used algorithms for data clustering and the usual clustering method for benchmarking.
Learning sparsity in reservoir computing through a novel bio-inspired algorithm
In this work we took inspiration from the fruit fly brain to formulate a novel machine learning algorithm that is able to optimize the sparsity level of a reservoir by changing the firing thresholds of the nodes.
A generalised framework for detailed classification of swimming paths inside the Morris Water Maze
The Morris Water Maze is commonly used in behavioural neuroscience for the study of spatial learning with rodents.
Is Epicurus the father of Reinforcement Learning?
The Epicurean Philosophy is commonly thought as simplistic and hedonistic.
Modelling Stock-market Investors as Reinforcement Learning Agents [Correction]
Decision making in uncertain and risky environments is a prominent area of research.
