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Found 6 papers, 4 papers with code
Neural General Circulation Models for Weather and Climate
Here we present the first GCM that combines a differentiable solver for atmospheric dynamics with ML components, and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best ML and physics-based methods.
WeatherBench 2: A benchmark for the next generation of data-driven global weather models
WeatherBench 2 is an update to the global, medium-range (1-14 day) weather forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to accelerate progress in data-driven weather modeling.
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware
Markov chain Monte Carlo (MCMC) is widely regarded as one of the most important algorithms of the 20th century.
Constructing High Precision Knowledge Bases with Subjective and Factual Attributes
In this work, we develop a method for constructing KBs with tunable precision--i. e., KBs that can be made to operate at a specific false positive rate, despite storing both difficult-to-evaluate subjective attributes and more traditional factual attributes.
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Hamiltonian Monte Carlo is a powerful algorithm for sampling from difficult-to-normalize posterior distributions.
TensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation.
