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Found 8 papers, 0 papers with code
Generative Diffusion Models for Lattice Field Theory
This study delves into the connection between machine learning and lattice field theory by linking generative diffusion models (DMs) with stochastic quantization, from a stochastic differential equation perspective.
Diffusion Models as Stochastic Quantization in Lattice Field Theory
In this work, we establish a direct connection between generative diffusion models (DMs) and stochastic quantization (SQ).
Applications of Machine Learning to Lattice Quantum Field Theory
There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies.
Towards a Shapley Value Graph Framework for Medical peer-influence
In other words, although contribution towards a certain prediction is highlighted by feature attribution methods, the relation between features and the consequence of intervention is not studied.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Quantum field theories, Markov random fields and machine learning
The transition to Euclidean space and the discretization of quantum field theories on spatial or space-time lattices opens up the opportunity to investigate probabilistic machine learning within quantum field theory.
Machine learning with quantum field theories
The precise equivalence between discretized Euclidean field theories and a certain class of probabilistic graphical models, namely the mathematical framework of Markov random fields, opens up the opportunity to investigate machine learning from the perspective of quantum field theory.
Quantum field-theoretic machine learning
We derive machine learning algorithms from discretized Euclidean field theories, making inference and learning possible within dynamics described by quantum field theory.
Extending machine learning classification capabilities with histogram reweighting
We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learning methods.
