Randomness in neural networks: an overview
S Scardapane, D Wang - Wiley Interdisciplinary Reviews: Data …, 2017 - Wiley Online Library
Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …
Reservoir computing approaches to recurrent neural network training
M Lukoševičius, H Jaeger - Computer science review, 2009 - Elsevier
Echo State Networks and Liquid State Machines introduced a new paradigm in artificial
recurrent neural network (RNN) training, where an RNN (the reservoir) is generated …
recurrent neural network (RNN) training, where an RNN (the reservoir) is generated …
Physical reservoir computing—an introductory perspective
K Nakajima - Japanese Journal of Applied Physics, 2020 - iopscience.iop.org
Understanding the fundamental relationships between physics and its information-
processing capability has been an active research topic for many years. Physical reservoir …
processing capability has been an active research topic for many years. Physical reservoir …
Experimental demonstration of reservoir computing on a silicon photonics chip
K Vandoorne, P Mechet, T Van Vaerenbergh… - Nature …, 2014 - nature.com
In today's age, companies employ machine learning to extract information from large
quantities of data. One of those techniques, reservoir computing (RC), is a decade old and …
quantities of data. One of those techniques, reservoir computing (RC), is a decade old and …
A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting
Wind speed forecasting is a promising solution to improve the efficiency of energy utilization.
In this study, a novel hybrid wind speed forecasting model is proposed. The whole modeling …
In this study, a novel hybrid wind speed forecasting model is proposed. The whole modeling …
An overview and comparative analysis of recurrent neural networks for short term load forecasting
The key component in forecasting demand and consumption of resources in a supply
network is an accurate prediction of real-valued time series. Indeed, both service …
network is an accurate prediction of real-valued time series. Indeed, both service …
State-dependent computations: spatiotemporal processing in cortical networks
DV Buonomano, W Maass - Nature Reviews Neuroscience, 2009 - nature.com
A conspicuous ability of the brain is to seamlessly assimilate and process spatial and
temporal features of sensory stimuli. This ability is indispensable for the recognition of …
temporal features of sensory stimuli. This ability is indispensable for the recognition of …
Learning function from structure in neuromorphic networks
The connection patterns of neural circuits in the brain form a complex network. Collective
signalling within the network manifests as patterned neural activity and is thought to support …
signalling within the network manifests as patterned neural activity and is thought to support …
Reservoir computing trends
Reservoir Computing (RC) is a paradigm of understanding and training Recurrent Neural
Networks (RNNs) based on treating the recurrent part (the reservoir) differently than the …
Networks (RNNs) based on treating the recurrent part (the reservoir) differently than the …
Growing echo-state network with multiple subreservoirs
J Qiao, F Li, H Han, W Li - IEEE transactions on neural …, 2016 - ieeexplore.ieee.org
An echo-state network (ESN) is an effective alternative to gradient methods for training
recurrent neural network. However, it is difficult to determine the structure (mainly the …
recurrent neural network. However, it is difficult to determine the structure (mainly the …