dgoekmen

dgoekmen's Stars

• tensorflow/tensorflow

  An Open Source Machine Learning Framework for Everyone

  Language:C++187k7.5k40.1k74.4k

• JuliaLang/julia

  The Julia Programming Language

  Language:Julia46.1k93124.5k5.5k

• jax-ml/jax

  Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

  Language:Python30.9k3345.8k2.8k

• tkipf/gcn

  Implementation of Graph Convolutional Networks in TensorFlow

  Language:Python7.2k1571942k

• soimort/translate-shell

  :speech_balloon: Command-line translator using Google Translate, Bing Translator, Yandex.Translate, etc.

  Language:Awk7k145455391

• google/neural-tangents

  Fast and Easy Infinite Neural Networks in Python

  Language:Jupyter Notebook2.3k62156227

• google/TensorNetwork

  A library for easy and efficient manipulation of tensor networks.

  Language:Python1.8k64354358

• tensorflow/gnn

  TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.

  Language:Python1.4k3688181

• jax-md/jax-md

  Differentiable, Hardware Accelerated, Molecular Dynamics

  Language:Jupyter Notebook1.2k46160200

• SJ001/AI-Feynman

  Language:Python6512774195

• PetarV-/DGI

  Deep Graph Infomax (https://arxiv.org/abs/1809.10341)

  Language:Python6331219136

• SciML/DataDrivenDiffEq.jl

  Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

  Language:Julia4082015857

• kach/gradient-descent-the-ultimate-optimizer

  Code for our NeurIPS 2022 paper

  Language:Python3655022

• snap-stanford/neural-subgraph-learning-GNN

  Language:Jupyter Notebook34693265

• JuliaGaussianProcesses/AbstractGPs.jl

  Abstract types and methods for Gaussian Processes.

  Language:Julia23399721

• hsinyuan-huang/predicting-quantum-properties

  Using very few measurements to predict properties in quantum many-body systems.

  Language:C++18711231

• RMT-TheoryAndPractice/RMT

  Language:Matlab1115236

• Lattice-Automation/seqfold

  nucleic acid folding

  Language:Python8672112

• ITensor/ITensorInfiniteMPS.jl

  A package for working with infinite matrix product states (MPS) with ITensor.

  Language:Mathematica3593022

• VectorInstitute/VariationalNeuralAnnealing

  A variational implementation of classical and quantum annealing using recurrent neural networks for the purpose of solving optimization problems.

  Language:Jupyter Notebook30304

• risilab/Autobahn

  Repository for Autobahn: Automorphism Based Graph Neural Networks

  Language:Python29502

• RSMI-NE/RSMI-NE

  A Python package for efficient optimisation of real-space renormalization group transformations using Tensorflow.

  Language:Python28393

• carlobaldassi/RRRMC.jl

  Reduced-Rejection-Rate Monte Carlo for Ising spin models

  Language:Julia17426

• sknepneklab/VMTutorial

  Lorentz Centre workshop tutorial on Vertex Model

  Language:C++61

• DundeeB/OOP_hard_sphere_event_chain

  Event chain algorithm for hard spheres in quasi two dimensions. Used in the creation of the paper: https://journals.aps.org/prl/accepted/5f071Y88Z6e16c73873350a5a1658409be2fd9e2b, which is also available in partial in https://arxiv.org/abs/2111.14895

  Language:Python5002

• mandadapu-group/parallel-swap-mc

  A plug-in for HOOMD-Blue, a particle simulation toolkit, that allows parallel Monte Carlo simulation of soft & hard continuous-polydisperse particles on CPUs.

  Language:C++4210

• muxitox/Dynamical-MF-Self-Attention

  This repository contains the code to reproduce the experiments performed in the Dynamical Mean-Field Theory of Self-Attention Neural Networks article.

  Language:Python4400

• nikolas-claussen/blender-tissue-cartography

  Extract surfaces from volumetric microscopy images and project volumetric data to planar textures using blender

  Language:Jupyter Notebook30

• truskett-group-ut/hs_swap_mc

  Swap monte carlo simulation of hard spheres

  Language:C++3

• Jalal-abu-ahmad/ECMC

  Event chain algorithm for hard spheres in quasi two dimensions. Used in the creation of the paper: https://journals.aps.org/prl/accepted/5f071Y88Z6e16c73873350a5a1658409be2fd9e2b, which is also available in partial in https://arxiv.org/abs/2111.14895

  Language:Python11