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A curated collection of Monte Carlo tree search research papers with implementations from top AI conferences.
A Python library built on JAX for studying many-body quantum systems using neural networks and machine learning.
A Python package for designing, simulating, and validating complex adaptive systems using Monte Carlo methods and parameter sweeps.
A JAX-powered probabilistic programming library focused on performant sampling methods for Bayesian inference on CPU, GPU, and TPU.
A parallel Monte Carlo and machine learning library for scientific inference, available in Python, MATLAB, Fortran, C++, and C.
A comprehensive Go toolkit for High Energy Physics (HEP) data analysis, simulation, and visualization.
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