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Title: An introduction to training algorithms for neuromorphic computing and on-line learning Abstract: Training neural networks implemented in neuromorphic hardware is challenging due to the dynamic, sparse, and local nature of the computations. This tutorial will describe some established gradient-based solutions to address these challenges in the context of real-valued recurrent neural networks and spiking neural networks. Insights into gradient-based training algorithms and associated autodifferentiation methods lead to online synaptic plasticity rules and the necessary assumptions to implement them in in-memory computing devices. The tutorial will conclude with methods that can be used to improve and optimize learning algorithms using meta-learning and other meta-optimization approaches.
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Reusable LaTeX resources (figures, lists, tables, equations) for documents (presentations, theses, papers).
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Code for generation & analysis of an AiiDA database of double transition metal impurity embeddings into the topological insulator Bi2Te3.
Fork of https://iffgit.fz-juelich.de/mozumder/Master_Thesis_Mozumder .
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Georg Brandl / vitess
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f.landmeyer / Pypulseq
GNU Affero General Public License v3.0Updated -
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PGI15-Teaching / BICE24 RevealJS
MIT LicenseUpdated