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Found 5 papers, 0 papers with code
Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics
This new approach can accurately learn probabilistic cellular automata processes in different conditions, even when the process is a probabilistic mixture of different chaotic rules.
Word2rate: training and evaluating multiple word embeddings as statistical transitions
We used a modified version of the negative sampling objective for our context words, modelling the context embeddings as a Taylor series of rate matrices.
Giant rectification in segmented, strongly interacting, spin chains despite the presence of perturbations
Finally we demonstrate that in the thermodynamic limit there is no perfect rectification when each of the two half chains is interacting.
Statistical Mechanics
A scheme for automatic differentiation of complex loss functions
For a real function, automatic differentiation is such a standard algorithm used to efficiently compute its gradient, that it is integrated in various neural network frameworks.
Finding Quantum Critical Points with Neural-Network Quantum States
Finding the precise location of quantum critical points is of particular importance to characterise quantum many-body systems at zero temperature.
