JAX implementation of Generalization and Exploration via Randomized Value Functions (Osband et al., 2016)
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Updated
May 30, 2021 - Python
JAX implementation of Generalization and Exploration via Randomized Value Functions (Osband et al., 2016)
Fitting source models to Radio Interferometric visibilities using stochastic gradient descent.
Comparisons between PyTorch and JAX (Flax)
Deep Reinforcement Learning with Jax
Common practices for distributed training using various backends
Code for various probabilistic deep learning models
A culmination of the tasks I will be undertaking as part of a PhD project around the TOLIMAN mission.
Gaussian processes with spherical harmonic features in JAX
An R Package for Ultra-fast Rerandomization Using a JAX Backend
Fine-grained, dynamic control of neural network topology in JAX.
Conversion of LAST library from JAX to PyTorch
Experiments in multi-architecture parallelism for deep learning with JAX
Access the Xspec models and corresponding JAX/XLA ops.
Jax, Flax, examples (ImageClassification, SemanticSegmentation, and more...)
A setup for solving shape optimization problems depending on a partial differential equation using boundary integral methods and the Python library JAX. The code was developed as part of a Bachelor thesis project in Engineering Mathematics at the Royal Institute of Technology in Spring 2024 by Rebecka Johansson and Asta Stensson.
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