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Found 8 papers, 6 papers with code
Machine learning phase transitions: Connections to the Fisher information
Despite the widespread use and success of machine-learning techniques for detecting phase transitions from data, their working principle and fundamental limits remain elusive.
Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion
Up to now, the scheme required training a distinct binary classifier for each possible splitting of the grid into two sides, resulting in a computational cost that scales linearly with the number of grid points.
Differentiating Metropolis-Hastings to Optimize Intractable Densities
We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers, allowing us to differentiate through probabilistic inference, even if the model has discrete components within it.
Automatic Differentiation of Programs with Discrete Randomness
Automatic differentiation (AD), a technique for constructing new programs which compute the derivative of an original program, has become ubiquitous throughout scientific computing and deep learning due to the improved performance afforded by gradient-based optimization.
AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia
No single Automatic Differentiation (AD) system is the optimal choice for all problems.
Control of Stochastic Quantum Dynamics by Differentiable Programming
We apply this approach to the state preparation and stabilization of a qubit subjected to homodyne detection.
Interpretable and unsupervised phase classification
Fully automated classification methods that yield direct physical insights into phase diagrams are of current interest.
Disordered Systems and Neural Networks Strongly Correlated Electrons Quantum Physics
Divergence of predictive model output as indication of phase transitions
We introduce a new method to identify phase boundaries in physical systems.
Statistical Mechanics Disordered Systems and Neural Networks Quantum Physics
