Bibliographic details on Off-Line Performance Maximisation in Feed-Forward Neural Networks by Applying Virtual Neurons and Covariance Transformations.
This thesis is a study of the generation of topographic mappings - dimension reducing transformations of data that preserve some element of geometric ...
Off-line performance maximisation in feed-forward neural networks by applying virtual neurons and covariance transformations. Abstract Optimisation of a feed ...
Mar 6, 2024 · Throughout the offline and online experiments, the performance of the neural network has been illustrated in different conditions. The ...
Abstract. The convergence of back-propagation learning is analyzed so as to explain common phenomenon observed by practitioners. Many.
A hidden layer, feed-forward neural network to simulate the concatenation of two functions f 1 and f 2 from reproducing kernel Hilbert spaces.
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Learning in neuronal networks has developed in many directions, in particular to reproduce cognitive tasks like image recognition and speech processing.
We introduce an unsupervised online learning objective using ... (A) 16 out of 99 “toy networks” with different correlation structure (horizontal ... A.9.2 ...
For a neural network comprising feedforward and lateral connections, a local learning rule is proposed that causes the lateral connections to learn directly ...
Missing: Virtual Transformations.
Feb 23, 2023 · However, it is unknown how they emerge. Here, using feedforward neural networks, we demonstrate that the learning of multiple tasks causes ...