Showing 21-40 of 54 projects
Alpa is a distributed training and serving framework for large-scale neural networks with auto-parallelization.
A high-performance Transformer library for accelerating AI models on NVIDIA GPUs, including low-precision support.
Faust is a functional programming language for signal processing and sound synthesis.
A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX.
A refreshing functional take on deep learning, compatible with popular ML libraries like TensorFlow and PyTorch.
An open-source project that explores the use of world models for reinforcement learning in diverse domains like Minecraft.
A Python library for building neural networks with JAX, providing an elegant and easy-to-use interface.
A probabilistic programming library powered by NumPy and JAX for Bayesian inference and MCMC sampling.
A JAX-based library for Monte Carlo tree search, a reinforcement learning technique useful for AI agents.
EasyLM is a one-stop solution for pre-training, fine-tuning, evaluating, and serving large language models (LLMs) in JAX/Flax.
A comprehensive security engine for Java applications, supporting various authentication and authorization frameworks.
A collection of Java EE 7 samples covering various technologies like CDI, JAX-RS, JSF, and more.
Neural Tangents is a Python library for fast and easy infinite neural networks using JAX.
An extensible deep learning library that leverages JAX for high-performance ML research and production use cases.
Optax is a gradient processing and optimization library for JAX, a high-performance machine learning framework.
Distributed GPU-accelerated framework for evolutionary computation and optimization algorithms.
Numerical differential equation solvers in JAX, with autodifferentiation and GPU support.
A deep learning package for many-body potential energy representation and molecular dynamics
A JAX-based research toolkit for building, editing, and visualizing neural networks for vibe coders.
Official code for a state-of-the-art score-based generative modeling approach using stochastic differential equations.
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