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
Moss is a Python library for Reinforcement Learning.
Conversion of LAST library from JAX to PyTorch
Jax and Flax Time Series Prediction Transformer
An R Package for Ultra-fast Rerandomization Using a JAX Backend
Fine-grained, dynamic control of neural network topology in JAX.
Jax, Flax, examples (ImageClassification, SemanticSegmentation, and more...)
Experiments in multi-architecture parallelism for deep learning with JAX
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