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of 42
pro vyhledávání: '"Manukian, Haik"'
To improve artificial intelligence/autonomous systems and help with treating neurological conditions, there's a requirement for artificial neuron hardware that mimics biological. We examine experimental artificial neurons with quantum tunneling memor
Externí odkaz:
http://arxiv.org/abs/2401.00958
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
Externí odkaz:
http://arxiv.org/abs/2102.08562
Publikováno v:
Communications Physics volume 3, Article number:105 (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:
http://arxiv.org/abs/2001.05559
Programming by example is the problem of synthesizing a program from a small set of input / output pairs. Recent works applying machine learning methods to this task show promise, but are typically reliant on generating synthetic examples for trainin
Externí odkaz:
http://arxiv.org/abs/1911.02624
Publikováno v:
Journal of Machine Learning Research 21(159), 2020
Many optimization problems can be cast into the maximum satisfiability (MAX-SAT) form, and many solvers have been developed for tackling such problems. To evaluate a MAX-SAT solver, it is convenient to generate hard MAX-SAT instances with known solut
Externí odkaz:
http://arxiv.org/abs/1905.05334
Akademický článek
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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:
http://arxiv.org/abs/1801.00512
Publikováno v:
Phys. Rev. Applied 9, 034029 (2018)
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:
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 sys
Externí odkaz:
http://arxiv.org/abs/1612.04316
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:
http://arxiv.org/abs/1305.4634