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Found 5 papers, 2 papers with code
Diag2Diag: Multi modal super resolution for physics discovery with application to fusion
This paper introduces a groundbreaking multi-modal neural network model designed for resolution enhancement, which innovatively leverages inter-diagnostic correlations within a system.
PyRCN: A Toolbox for Exploration and Application of Reservoir Computing Networks
In this paper, we show how to uniformly describe RCNs with small and clearly defined building blocks, and we introduce the Python toolbox PyRCN (Python Reservoir Computing Networks) for optimizing, training and analyzing RCNs on arbitrarily large datasets.
Cluster-based Input Weight Initialization for Echo State Networks
Echo State Networks (ESNs) are a special type of recurrent neural networks (RNNs), in which the input and recurrent connections are traditionally generated randomly, and only the output weights are trained.
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems
Another concern that is often cited when designing smart home applications is the resilience of these applications against cyberattacks.
Phoneme Recognition with Large Hierarchical Reservoirs
Automatic speech recognition has gradually improved over the years, but the reliable recognition of unconstrained speech is still not within reach.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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