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Found 7 papers, 1 papers with code
Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness
For sensitive problems, such as medical imaging or fraud detection, Neural Network (NN) adoption has been slow due to concerns about their reliability, leading to a number of algorithms for explaining their decisions.
Ranked #1 on
Robust classification
on CIFAR-10
Memory and Information Processing in Recurrent Neural Networks
Recurrent neural networks (RNN) are simple dynamical systems whose computational power has been attributed to their short-term memory.
Hierarchical Composition of Memristive Networks for Real-Time Computing
Reservoir computing is an approach that takes advantage of collective system dynamics for real-time computing.
Learning Two-input Linear and Nonlinear Analog Functions with a Simple Chemical System
To the best of our knowledge, it is the first simulated chemical system capable of doing so.
A Comparative Study of Reservoir Computing for Temporal Signal Processing
Reservoir computing (RC) is a novel approach to time series prediction using recurrent neural networks.
Learning, Generalization, and Functional Entropy in Random Automata Networks
It has been shown \citep{broeck90:physicalreview, patarnello87:europhys} that feedforward Boolean networks can learn to perform specific simple tasks and generalize well if only a subset of the learning examples is provided for learning.
Computational Capabilities of Random Automata Networks for Reservoir Computing
We study the relationship between dynamics and computational capability in Random Boolean Networks (RBN) for Reservoir Computing (RC).
