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We present a nonparametric construction for deep learning compatible modern Hopfield models and utilize this framework to debut an efficient variant. Our key contribution stems from interpreting the memory storage and retrieval processes in modern Ho
Externí odkaz:
http://arxiv.org/abs/2404.03900
We propose a two-stage memory retrieval dynamics for modern Hopfield models, termed $\mathtt{U\text{-}Hop}$, with enhanced memory capacity. Our key contribution is a learnable feature map $\Phi$ which transforms the Hopfield energy function into kern
Externí odkaz:
http://arxiv.org/abs/2404.03827
We present STanHop-Net (Sparse Tandem Hopfield Network) for multivariate time series prediction with memory-enhanced capabilities. At the heart of our approach is STanHop, a novel Hopfield-based neural network block, which sparsely learns and stores
Externí odkaz:
http://arxiv.org/abs/2312.17346
We introduce the sparse modern Hopfield model as a sparse extension of the modern Hopfield model. Like its dense counterpart, the sparse modern Hopfield model equips a memory-retrieval dynamics whose one-step approximation corresponds to the sparse a
Externí odkaz:
http://arxiv.org/abs/2309.12673
Current methods for generating attractive headlines often learn directly from data, which bases attractiveness on the number of user clicks and views. Although clicks or views do reflect user interest, they can fail to reveal how much interest is rai
Externí odkaz:
http://arxiv.org/abs/2306.14828
Autor:
Beard, Diana C., Zhang, Xiyun, Wu, Dennis Y., Martin, Jenna R., Erickson, Alyssa, Boua, Jane Valeriane, Hamagami, Nicole, Swift, Raylynn G., McCullough, Katherine B., Ge, Xia, Bell-Hensley, Austin, Zheng, Hongjun, Palmer, Cory W., Fuhler, Nicole A., Lawrence, Austin B., Hill, Cheryl A., Papouin, Thomas, Noguchi, Kevin K., McAlinden, Audrey, Garbow, Joel R., Dougherty, Joseph D., Maloney, Susan E., Gabel, Harrison W.
Publikováno v:
In Cell Reports 28 November 2023 42(11)
Akademický článek
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Autor:
Wu, Dennis M.
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2008.
Includes bibliographical references (p. 236-242).
Includes bibliographical references (p. 236-242).
Externí odkaz:
http://dspace.mit.edu/handle/1721.1/42421
Includes bibliographical references (p. 236-242).
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2008.
A new thermal to electric conversion scheme based on an excitatio
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2008.
A new thermal to electric conversion scheme based on an excitatio
Externí odkaz:
http://hdl.handle.net/1721.1/42421
Autor:
Wu, Dennis.
Thesis (M.Sc.)--University of Toronto, 2005.
Source: Masters Abstracts International, Volume: 44-01, page: 0497.
Source: Masters Abstracts International, Volume: 44-01, page: 0497.