Differentiable Programming
Showing 13 of 13 projects
A differentiable computer vision library for PyTorch, providing geometric vision and image processing algorithms for AI workflows.
An archived experiment integrating TensorFlow's machine learning capabilities directly into the Swift programming language with first-class differentiable programming.
A library of differentiable digital signal processing functions for interpretable audio synthesis in deep learning models.
A JAX library for neural networks and scientific computing with PyTorch-like syntax and full ecosystem compatibility.
An open-source differentiable dense SLAM library for PyTorch, enabling gradient flow from map outputs to sensor inputs.
Hardware-accelerated, batchable, and differentiable optimization algorithms implemented in JAX for machine learning research.
A tensor library for differentiable functional programming in F#, with PyTorch-like APIs and GPU support.
A differentiable cosmology library built with JAX for automatic differentiation of cosmological calculations.
A JAX-based research framework for differentiable and parallelizable acoustic simulations, running on CPU, GPU, and TPU.
A JAX-based framework for building differentiable numerical simulators with arbitrary discretizations for physical systems.
A JAX-based differentiable spectral modeling library for exoplanets, brown dwarfs, and M dwarfs.
A differentiable hydrodynamics and magnetohydrodynamics code for astrophysics built with JAX, enabling gradient-based inverse modeling and multi-GPU simulations.
A differentiable ray tracing toolbox for radio propagation simulations, built on JAX for optimization and machine learning.
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