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Tesi etd-03292019-220853


Tipo di tesi
Tesi di laurea magistrale
Autore
LAGANI, GABRIELE
Indirizzo email
gabriele.lagani@gmail.com
URN
etd-03292019-220853
Titolo
Hebbian Learning Algorithms for Training Convolutional Neural Networks
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
COMPUTER ENGINEERING
Relatori
relatore Amato, Giuseppe
relatore Falchi, Fabrizio
relatore Gennaro, Claudio
Parole chiave
  • computer
  • convolutional
  • deep
  • Hebbian
  • learning
  • networks
  • neural
  • python
  • pytorch
  • vision
Data inizio appello
03/05/2019
Consultabilità
Completa
Riassunto
The concept of Hebbian learning refers to a family of learning rules, inspired by biology, according to which the weight associated with a synapse increases proportionally to the values of the pre-synaptic and post-synaptic stimuli at a given instant of time. Different variants of Hebbian rules can be found in literature. In this thesis, three main Hebbian learning approaches are explored: Winner-Takes-All competition, Self-Organizing Maps and a supervised Hebbian learning solution for training the final classification layer of a network. In literature, applications of Hebbian learning rules to train networks for image classification tasks exist, although they are currently limited to relatively shallow architectures. In this thesis, the possibility of applying Hebbian learning rules to deeper network architectures is explored and the results are compared to those achieved with Gradient Descent on the same architectures.
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