Automatically differentiable Hartree-Fock
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Updated
Nov 22, 2022 - Python
Automatically differentiable Hartree-Fock
Automatic differentiation engine for neural network training (backprop under development).
A differentiable Python version of NOAA-OWP / cfe using PyTorch, for research and development
Basic RL
python package for fast solver-free training of neural ILP architectures
A prototype differentiable programming language with an integration primitive that soundly handles interactions among the derivative, integral, and discontinuities and supports separate compilation.
PyTorch and Taichi implementations of our paper on improving gradient computation
Differentiable Symbolic Specification
Differentiable and accelerated wavelet transform on the ball with JAX
A prototype framework for developing multi-step machine learnings.
👀🔥 Differentiable foveated vision for Deep Learning methods
A differentiable program for mapping brain function
NeurIPS'21 Differentiable Program Synthesis
🚃 Automated task/test framework for writing differentiable software.
Constrained Hierarchical Deep Reinforcement Learning with Differentiable Formal Specifications
Code for our paper Demand Response Model Identification and Behavior Forecast with OptNet: a Gradient-based Approach.
variationally solve hydrogen atom with autograd
program synthesis with neuro-symbolic differentiable interpreters
Applications of the Teg differentiable programming language to problems spanning graphics and physical simulation.
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