Author: "Gennaro, Claudio" / Topic: neural networks - Searchworks@Jio Institute Digital Library Search Results

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Start Over Author "Gennaro, Claudio" Topic neural networks

Author: Lagani, Gabriele, Gennaro, Claudio, Fassold, Hannes, Amato, Giuseppe, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Skopal, Tomáš, editor, Falchi, Fabrizio, editor, Lokoč, Jakub, editor, Sapino, Maria Luisa, editor, Bartolini, Ilaria, editor, and Patella, Marco, editor
Published: 2022
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Author: Lagani, Gabriele, Falchi, Fabrizio, Gennaro, Claudio, Amato, Giuseppe, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nicosia, Giuseppe, editor, Ojha, Varun, editor, La Malfa, Emanuele, editor, La Malfa, Gabriele, editor, Jansen, Giorgio, editor, Pardalos, Panos M., editor, Giuffrida, Giovanni, editor, and Umeton, Renato, editor
Published: 2022
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Author: Lagani, Gabriele, Falchi, Fabrizio, Gennaro, Claudio, Amato, Giuseppe, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nicosia, Giuseppe, editor, Ojha, Varun, editor, La Malfa, Emanuele, editor, La Malfa, Gabriele, editor, Jansen, Giorgio, editor, Pardalos, Panos M., editor, Giuffrida, Giovanni, editor, and Umeton, Renato, editor
Published: 2022
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Author: Lagani, Gabriele, Falchi, Fabrizio, Gennaro, Claudio, and Amato, Giuseppe
Published: 2022
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Author: Bolettieri, Paolo, Carrara, Fabio, Debole, Franca, Falchi, Fabrizio, Gennaro, Claudio, Vadicamo, Lucia, Vairo, Claudio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Amato, Giuseppe, editor, Gennaro, Claudio, editor, Oria, Vincent, editor, and Radovanović, Miloš, editor
Published: 2019
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Author: Lagani, Gabriele, Falchi, Fabrizio, Gennaro, Claudio, Fassold, Hannes, and Amato, Giuseppe
Subjects: *ARTIFICIAL neural networks, *PRINCIPAL components analysis, *COMPUTER vision, *MATRIX multiplications
Abstract: Recent work on sample efficient training of Deep Neural Networks (DNNs) proposed a semi-supervised methodology based on biologically inspired Hebbian learning, combined with traditional backprop-based training. Promising results were achieved on various computer vision benchmarks, in scenarios of scarce labeled data availability. However, current Hebbian learning solutions can hardly address large-scale scenarios due to their demanding computational cost. In order to tackle this limitation, in this contribution, we investigate a novel solution, named FastHebb (FH), based on the reformulation of Hebbian learning rules in terms of matrix multiplications, which can be executed more efficiently on GPU. Starting from Soft-Winner-Takes-All (SWTA) and Hebbian Principal Component Analysis (HPCA) learning rules, we formulate their improved FH versions: SWTA-FH and HPCA-FH. We experimentally show that the proposed approach accelerates training speed up to 70 times, allowing us to gracefully scale Hebbian learning experiments on large datasets and network architectures such as ImageNet and VGG. [ABSTRACT FROM AUTHOR]
Published: 2024
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