A JIT compiler for hybrid quantum programs in PennyLane
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Updated
Aug 30, 2024 - Python
A JIT compiler for hybrid quantum programs in PennyLane
Trace, the New AutoDiff for AI Systems and LLM Agents
Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.
Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.
TensorFlow implementation of differentiable LQ matrix decomposition for all matrix orders.
Drop-in autodiff for NumPy.
Differentiable optical models as parameterised neural networks in Jax using Zodiax
Automatic optimisation of a Parallel-Plate Avalanche Counter with Optical Readout
Basic tools for MR simulation, infrastructure of arXiv:2008.10594 / tmi.2021.3083104
AutoBound automatically computes upper and lower bounds on functions.
Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
Compressible Euler equations solved with finite volume implemented in JAX, plugged into an optimization loop
Version 2.0 of Kineverse, a framework for modeling kinematics for robotic manipulation and control.
JAX Implementation of Differentiable NLP Solvers
Fuzzing Automatic Differentiation in Deep-Learning Libraries (ICSE'23)
Gradphi: Our linear algebra, autodiff and machine learning playground. It's a bit wild, but that's how we are.
Yet another tensor automatic differentiation framework
Geometry processing utilities compatible with jax for autodifferentiation.
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