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Found 6 papers, 2 papers with code
Mode-Assisted Joint Training of Deep Boltzmann Machines
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.
Mode-Assisted Unsupervised Learning of Restricted Boltzmann Machines
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.
Data Generation for Neural Programming by Example
A particular challenge lies in generating meaningful sets of inputs and outputs, which well-characterize a given program and accurately demonstrate its behavior.
Generating Weighted MAX-2-SAT Instances of Tunable Difficulty with Frustrated Loops
Many optimization problems can be cast into the maximum satisfiability (MAX-SAT) form, and many solvers have been developed for tackling such problems.
Accelerating Deep Learning with Memcomputing
In fact, the acceleration of pretraining achieved by simulating DMMs is comparable to, in number of iterations, the recently reported hardware application of the quantum annealing method on the same network and data set.
Memcomputing Numerical Inversion with Self-Organizing Logic Gates
We propose to use Digital Memcomputing Machines (DMMs), implemented with self-organizing logic gates (SOLGs), to solve the problem of numerical inversion.
