EverettGrethel
Researcher at Lawrence Livermore National Laboratory
Lawrence Livermore National Laboratory, @LLNLLivermore, CA
EverettGrethel's Stars
pytorch/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
fastai/fastai
The fastai deep learning library
AntixK/PyTorch-VAE
A Collection of Variational Autoencoders (VAE) in PyTorch.
DEAP/deap
Distributed Evolutionary Algorithms in Python
Trusted-AI/adversarial-robustness-toolbox
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
cornellius-gp/gpytorch
A highly efficient implementation of Gaussian Processes in PyTorch
MilesCranmer/PySR
High-Performance Symbolic Regression in Python and Julia
torchgan/torchgan
Research Framework for easy and efficient training of GANs based on Pytorch
Bjarten/early-stopping-pytorch
Early stopping for PyTorch
bckenstler/CLR
SJ001/AI-Feynman
MilesCranmer/SymbolicRegression.jl
Distributed High-Performance Symbolic Regression in Julia
kwotsin/mimicry
[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.
dso-org/deep-symbolic-optimization
A deep learning framework for symbolic optimization.
chinasatokolo/csGraduateFellowships
A curated list of fellowships for graduate students in Computer Science and related fields.
KristofferC/NearestNeighbors.jl
High performance nearest neighbor data structures (KDTree and BallTree) and algorithms for Julia.
jeffdonahue/bigan
code for "Adversarial Feature Learning"
cavalab/srbench
A living benchmark framework for symbolic regression
EliLillyCo/LillyMol
LillyMol Public Code
xuehaouwa/SS-LSTM
SS-LSTM model for pedestrian trajectory prediction
JuliaMath/Sobol.jl
generation of Sobol low-discrepancy sequence (LDS) for the Julia language
swyoon/pytorch-minimal-gaussian-process
A minimal implementation of Gaussian process regression in PyTorch
LironSimon/SciMED
A computational framework for finding symbolic expressions from physical datasets.
MrUrq/LatinHypercubeSampling.jl
Julia package for the creation of optimised Latin Hypercube Sampling Plans
coleygroup/del_qsar
chenhongge/treeVerification
[NeurIPS 2019] H. Chen*, H. Zhang*, S. Si, Y. Li, D. Boning and C.-J. Hsieh, Robustness Verification of Tree-based Models (*equal contribution)
druidowm/OccamNet_Public
flexgp/efs
Evolutionary feature synthesis
jamflcgh/edesigner_core
KindXiaoming/sid
discovering interpretable conservation laws from differential equations