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Found 4 papers, 1 papers with code
Parallelized Computation and Backpropagation Under Angle-Parametrized Orthogonal Matrices
We present a methodology for parallel acceleration of learning in the presence of matrix orthogonality and unitarity constraints of interest in several branches of machine learning.
Chook -- A comprehensive suite for generating binary optimization problems with planted solutions
We present Chook, an open-source Python-based tool to generate discrete optimization problems of tunable complexity with a priori known solutions.
Quantum Physics Disordered Systems and Neural Networks Other Computer Science
Discrete Equilibrium Sampling with Arbitrary Nonequilibrium Processes
We present a novel framework for performing statistical sampling, expectation estimation, and partition function approximation using \emph{arbitrary} heuristic stochastic processes defined over discrete state spaces.
A Stochastic approximation method for inference in probabilistic graphical models
We describe a new algorithmic framework for inference in probabilistic models, and apply it to inference for latent Dirichlet allocation.
