Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Haik Manukian"'
Publikováno v:
AIP Advances, Vol 14, Iss 8, Pp 085228-085228-10 (2024)
To improve artificial intelligence/autonomous systems and help with treating neurological conditions, there is a requirement for the discovery and design of artificial neuron hardware that mimics the advanced functionality and operation of the neural
Externí odkaz:
https://doaj.org/article/fa474076ff1e4d409dc8320ba5998df7
Autor:
Haik Manukian, Massimiliano Di Ventra
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
Abstract The deep extension of the restricted Boltzmann machine (RBM), known as the deep Boltzmann machine (DBM), is an expressive family of machine learning models which can serve as compact representations of complex probability distributions. Howe
Externí odkaz:
https://doaj.org/article/95741509c9624ceab2c63fcbc316be4c
Autor:
Massimiliano Di Ventra, Haik Manukian
Publikováno v:
Scientific Reports
Scientific reports, vol 11, iss 1
Scientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
Scientific reports, vol 11, iss 1
Scientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
The deep extension of the restricted Boltzmann machine (RBM), known as the deep Boltzmann machine (DBM), is an expressive family of machine learning models which can serve as compact representations of complex probability distributions. However, join
Publikováno v:
Communications Physics, Vol 3, Iss 1, Pp 1-8 (2020)
Restricted Boltzmann machines (RBMs) are a powerful class of generative models, but their training requires computing a gradient that, unlike supervised backpropagation on typical loss functions, is notoriously difficult even to approximate. Here, we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73e79f7358fc1583999b8403e3c5b5c4
http://arxiv.org/abs/2001.05559
http://arxiv.org/abs/2001.05559
Restricted Boltzmann machines (RBMs) and their extensions, called 'deep-belief networks', are powerful neural networks that have found applications in the fields of machine learning and artificial intelligence. The standard way to training these mode
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f7ea2a460b32dd366e6c91bcc4970308
Autor:
Yan Ru Pei1 YRPEI@UCSD.EDU, Haik Manukian1 HMANUKIA@UCSD.EDU, Di Ventra, Massimiliano1 DIVENTRA@PHYSICS.UCSD.EDU
Publikováno v:
Journal of Machine Learning Research. 2020, Vol. 21 Issue 146-188, p1-55. 55p.
Self-organizing logic is a recently-suggested framework that allows the solution of Boolean truth tables "in reverse," i.e., it is able to satisfy the logical proposition of gates regardless to which terminal(s) the truth value is assigned ("terminal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e1a9300b626a79c9421aebf98c61e9e
http://arxiv.org/abs/1708.08949
http://arxiv.org/abs/1708.08949
We propose to use digital memcomputing machines (DMMs), implemented with self-organizing logic gates (SOLGs), to solve the problem of numerical inversion. Starting from fixed-point scalar inversion, we describe the generalization to solving linear sy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54b711e396e849ee65d69515a427b482
When a star comes within a critical distance to a supermassive black hole (SMBH), immense tidal forces disrupt the star, resulting in a stream of debris that falls back onto the SMBH and powers a luminous flare. In this paper, we perform hydrodynamic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b4212d828c2015a6d1c56ed3dea488d
http://arxiv.org/abs/1304.6397
http://arxiv.org/abs/1304.6397
The centers of most known galaxies host supermassive black holes (SMBHs). In orbit around these black holes are a centrally-concentrated distribution of stars, both in single and in binary systems. Occasionally, these stars are perturbed onto orbits
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca24b7c0c99a743358a654aae8f43cb4