AIMC Topic: Learning

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Forgetting memristor based STDP learning circuit for neural networks.

Neural networks : the official journal of the International Neural Network Society
The circuit implementation of STDP based on memristor is of great significance for the application of neural network. However, recent research shows that the research on the pure circuit implementation of forgetting memristor and STDP is still rare. ...

Self-Supervision-Augmented Deep Autoencoder for Unsupervised Visual Anomaly Detection.

IEEE transactions on cybernetics
Deep autoencoder (AE) has demonstrated promising performances in visual anomaly detection (VAD). Learning normal patterns on normal data, deep AE is expected to yield larger reconstruction errors for anomalous samples, which is utilized as the criter...

Deep Semisupervised Multiview Learning With Increasing Views.

IEEE transactions on cybernetics
In this article, we study two challenging problems in semisupervised cross-view learning. On the one hand, most existing methods assume that the samples in all views have a pairwise relationship, that is, it is necessary to capture or establish the c...

Sparse Bayesian Learning Based on Collaborative Neurodynamic Optimization.

IEEE transactions on cybernetics
Regression in a sparse Bayesian learning (SBL) framework is usually formulated as a global optimization problem with a nonconvex objective function and solved in a majorization-minimization framework where the solution quality and consistency depend ...

Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation.

PLoS computational biology
Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new training is interleaved with period...

Reinforcement learning for robust stabilization of nonlinear systems with asymmetric saturating actuators.

Neural networks : the official journal of the International Neural Network Society
We study the robust stabilization problem of a class of nonlinear systems with asymmetric saturating actuators and mismatched disturbances. Initially, we convert such a robust stabilization problem into a nonlinear-constrained optimal control problem...

Acquisition of chess knowledge in AlphaZero.

Proceedings of the National Academy of Sciences of the United States of America
We analyze the knowledge acquired by AlphaZero, a neural network engine that learns chess solely by playing against itself yet becomes capable of outperforming human chess players. Although the system trains without access to human games or guidance,...

One-Class Convolutional Neural Networks for Water-Level Anomaly Detection.

Sensors (Basel, Switzerland)
Companies that own water systems to provide water storage and distribution services always strive to enhance and efficiently distribute water to different places for various purposes. However, these water systems are likely to face problems ranging f...

Revisiting graph neural networks from hybrid regularized graph signal reconstruction.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) have shown strong graph-structured data processing capabilities. However, most of them are generated based on the message-passing mechanism and lack of the systematic approach to guide their developments. Meanwhile, a uni...

Achieving small-batch accuracy with large-batch scalability via Hessian-aware learning rate adjustment.

Neural networks : the official journal of the International Neural Network Society
We consider synchronous data-parallel neural network training with a fixed large batch size. While the large batch size provides a high degree of parallelism, it degrades the generalization performance due to the low gradient noise scale. We propose ...