315 citations · 479 across the 8 of their papers we have counts for
5 papers · 1 filter
Mode-Assisted Joint Training of Deep Boltzmann Machines
Haik Manukian, Massimiliano Di Ventra
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 compa…
Mode-Assisted Unsupervised Learning of Restricted Boltzmann Machines
Haik Manukian, Yan Ru Pei, Sean R. B. Bearden +1
Restricted Boltzmann machines (RBMs) are a powerful class of generative models, but their training requires computing a gradient that, unlike supervised backpropagation on typical…
Data Generation for Neural Programming by Example
Judith Clymo, Haik Manukian, Nathanaël Fijalkow +2
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,…
Generating Weighted MAX-2-SAT Instances of Tunable Difficulty with Frustrated Loops
Yan Ru Pei, Haik Manukian, Massimiliano Di Ventra
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…
Accelerating Deep Learning with Memcomputing
Haik Manukian, Fabio L. Traversa, Massimiliano Di Ventra
Restricted Boltzmann machines (RBMs) and their extensions, called 'deep-belief networks', are powerful neural networks that have found applications in the fields of machine learnin…