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pro vyhledávání: '"Dwivedi, Kshitij"'
We introduce Net2Brain, a graphical and command-line user interface toolbox for comparing the representational spaces of artificial deep neural networks (DNNs) and human brain recordings. While different toolboxes facilitate only single functionaliti
Externí odkaz:
http://arxiv.org/abs/2208.09677
Today's state of the art visual navigation agents typically consist of large deep learning models trained end to end. Such models offer little to no interpretability about the learned skills or the actions of the agent taken in response to its enviro
Externí odkaz:
http://arxiv.org/abs/2206.08500
Autor:
Dwivedi, Kshitij1,2 (AUTHOR), Sadiya, Sari2,3 (AUTHOR) Saba-Sadiya@em.uni-frankfurt.de, Balode, Marta P.1,4 (AUTHOR), Roig, Gemma2,5 (AUTHOR), Cichy, Radoslaw M.1 (AUTHOR)
Publikováno v:
Scientific Reports. 3/6/2024, Vol. 14 Issue 1, p1-7. 7p.
In this paper, we tackle an open research question in transfer learning, which is selecting a model initialization to achieve high performance on a new task, given several pre-trained models. We propose a new highly efficient and accurate approach ba
Externí odkaz:
http://arxiv.org/abs/2008.02107
Autor:
Cichy, Radoslaw Martin, Roig, Gemma, Andonian, Alex, Dwivedi, Kshitij, Lahner, Benjamin, Lascelles, Alex, Mohsenzadeh, Yalda, Ramakrishnan, Kandan, Oliva, Aude
In the last decade, artificial intelligence (AI) models inspired by the brain have made unprecedented progress in performing real-world perceptual tasks like object classification and speech recognition. Recently, researchers of natural intelligence
Externí odkaz:
http://arxiv.org/abs/1905.05675
Autor:
Dwivedi, Kshitij, Roig, Gemma
Transfer learning is widely used in deep neural network models when there are few labeled examples available. The common approach is to take a pre-trained network in a similar task and finetune the model parameters. This is usually done blindly witho
Externí odkaz:
http://arxiv.org/abs/1904.11740
Convolutional Neural Networks (CNNs) have been proven to be extremely successful at solving computer vision tasks. State-of-the-art methods favor such deep network architectures for its accuracy performance, with the cost of having massive number of
Externí odkaz:
http://arxiv.org/abs/1904.09764
Akademický článek
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Publikováno v:
In NeuroImage 1 December 2022 264
Publikováno v:
Journal of Cognitive Neuroscience. Oct2021, Vol. 33 Issue 10, p2032-2043. 12p. 2 Diagrams, 2 Charts, 2 Graphs.