Hitchhiker's Guide to Deep Learning for Computational Scientists
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Updated
Nov 26, 2018 - Python
Hitchhiker's Guide to Deep Learning for Computational Scientists
variationally solve hydrogen atom with autograd
A reproduction and tweaking of Growing Neural Cellular Automata
🚃 Automated task/test framework for writing differentiable software.
A GPU-accelerated differentiable fluid simulator written in JAX.
Automatic differentiation engine for neural network training (backprop under development).
A fast and highly accurate differentiable Top-k operator from the "Successive Halving Top-k Operator" AAAI'21 paper.
[ICLR 2021] "Generating Adversarial Computer Programs using Optimized Obfuscations" by Shashank Srikant, Sijia Liu, Tamara Mitrovska, Shiyu Chang, Quanfu Fan, Gaoyuan Zhang, and Una-May O'Reilly
NeurIPS'21 Differentiable Program Synthesis
Applications of the Teg differentiable programming language to problems spanning graphics and physical simulation.
Code for our paper Demand Response Model Identification and Behavior Forecast with OptNet: a Gradient-based Approach.
Safe Pontryagin Differentiable Programming (Safe PDP) is a new theoretical and algorithmic safe differentiable framework to solve a broad class of safety-critical learning and control tasks.
[ICLR 2021 top 3%] Is Attention Better Than Matrix Decomposition?
A framework and method to jointly learn a (neural) control objective function and a time-warping function only from sparse demonstrations or waypoints.
Constrained Hierarchical Deep Reinforcement Learning with Differentiable Formal Specifications
Automatically differentiable Hartree-Fock
Code for our NeurIPS 2022 paper
ICLR'22 Programmatic Reinforcement Learning
python package for fast solver-free training of neural ILP architectures
Basic RL
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