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This behavior is particularly true in the case of nanoindentation testing.
ANALYSIS OF THE GENERALIZED RECIRCULATION-BASED LEARNING ALGORITHMS IN BIDIRECTIONAL NEURAL NETWORKS Computational Analysis of the Bidirectional Activation-based Learning in Autoencoder Task [article IJCNN, pdf] Master thesis [pdf] Public git repository with source code[git] In our work, we used computational simulations to analyse supervised artificial neural networks based on the Generalized recirculation algorithm (Gene Rec) by O’Reilly (1996) and the Bidirectional Activation-based Learning algorithm (BAL) by Farkaš and Rebrová (2013).
The main idea of both algorithms is to update weights based on the difference between forward and backward propagation of neuron activations rather than based on error backpropagation (BP) between layers, which is considered biologically implausible.
Fused Silica is an amorphous crystal that can show plastic behavior at micro-scale despite its brittle behavior in large scales.
Due to the amorphous and ductile nature of Fused Silica, this behavior may not be explained well using the traditional dislocation-based mechanism of plasticity for crystalline solids.
Building on ideas from previous research on source separation, we propose an algorithm using a deep neural network with convolutional layers.
This type of neural network has resulted in state-of-the-art techniques for several image processing problems.Ever notice that sometimes the neural networks on this blog do a better job of imitating weird datasets than at other times?Here are two major things that affect how convincing a neural network version will be:1. How easy the dataset is to fake So, the neural network’s beer names were usually indistinguishable from human-generated names: I had lots of examples, and the names are silly to begin with.Each word is chosen with care, but only someone with knowledge of the field would notice a mistake.This makes thesis titles delightfully easy to fake.However, both algorithms struggle to learn low dimensional mappings which could be easily learned by BP. Several modifications of BAL are proposed and after systematic analysis a Two learning rates (TLR) version is introduced.TLR uses different learning rates for different weight matrices.The system’s performance is comparable with other state-ofthe- art algorithms like non-negative matrix factorization, in terms of separation performance, while improving significantly on processing time.This thesis is a stepping block for further research in this area, particularly for implementation of source separation algorithms for medical purposes like speech enhancement for cochlear implants, a task that requires low-latency.The Mixing Secrets Dataset 100 (MSD100) and the Demixing Secrets Dataset 100 (DSD100) are used for evaluation of the methodology .The results achieved by the algorithm show a 8.4 d B gain in SDR and a 9 d B gain in SAR for vocals over the state-of-the-art deep learning source separation approach using recurrent neural networks.