Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Rivkind, Alexander"'
To be effective in sequential data processing, Recurrent Neural Networks (RNNs) are required to keep track of past events by creating memories. While the relation between memories and the network's hidden state dynamics was established over the last
Externí odkaz:
http://arxiv.org/abs/1902.07275
Autor:
Rivkind, Alexander, Barak, Omri
Publikováno v:
Phys. Rev. Lett. 118, 258101 (2017)
Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task related neural dynamics we study trained Recurrent Neural Networks. We develop a Mean Field Theory for Reservoir Computing networks tr
Externí odkaz:
http://arxiv.org/abs/1511.05222
We show that the spacing between eigenvalues of the discrete 1D Hamiltonian with arbitrary potentials which are bounded, and with Dirichlet or Neumann Boundary Conditions is bounded away from zero. We prove an explicit lower bound, given by $Ce^{-bN}
Externí odkaz:
http://arxiv.org/abs/1102.2109
Autor:
Rivkind, Alexander1,2 (AUTHOR), Schreier, Hallel2,3 (AUTHOR), Brenner, Naama2,4 (AUTHOR) nbrenner@technion.ac.il, Barak, Omri1,2 (AUTHOR)
Publikováno v:
PLoS Computational Biology. 5/11/2020, Vol. 16 Issue 5, p1-24. 24p. 1 Chart, 10 Graphs.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Amy F. Stempler
Publikováno v:
Judaica Librarianship. 16:137-147
Isaac Edward Kiev (1905–1975), former Chief Librarian of New York’s Hebrew Union College-Jewish Institute of Religion, spent a lifetime facilitating Jewish research. This article, based on the author’s Master’s thesis on Kiev, focuses on his
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b1199810126c56f5d9b1f05245ab215e
https://doi.org/10.14263/2330-2976.1009
https://doi.org/10.14263/2330-2976.1009
The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and rein