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pro vyhledávání: '"Farinha, Matilde Tristany"'
Autor:
Farinha, Matilde Tristany, Ortner, Thomas, Dellaferrera, Giorgia, Grewe, Benjamin, Pantazi, Angeliki
Artificial Neural Networks (ANNs) trained with Backpropagation (BP) excel in different daily tasks but have a dangerous vulnerability: inputs with small targeted perturbations, also known as adversarial samples, can drastically disrupt their performa
Externí odkaz:
http://arxiv.org/abs/2309.17348
Autor:
Meulemans, Alexander, Farinha, Matilde Tristany, Cervera, Maria R., Sacramento, João, Grewe, Benjamin F.
The success of deep learning ignited interest in whether the brain learns hierarchical representations using gradient-based learning. However, current biologically plausible methods for gradient-based credit assignment in deep neural networks need in
Externí odkaz:
http://arxiv.org/abs/2204.07249
Autor:
Meulemans, Alexander, Farinha, Matilde Tristany, Ordóñez, Javier García, Aceituno, Pau Vilimelis, Sacramento, João, Grewe, Benjamin F.
The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output. However, the majority of current attempts at biologically-p
Externí odkaz:
http://arxiv.org/abs/2106.07887
Artificial neural networks, one of the most successful approaches to supervised learning, were originally inspired by their biological counterparts. However, the most successful learning algorithm for artificial neural networks, backpropagation, is c
Externí odkaz:
http://arxiv.org/abs/2006.08798
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