mekty2012's Stars
lezcano/geotorch
Constrained optimization toolkit for PyTorch
hongseok-yang/logic24
ssimplexity/CS492_spring2024
KAIST. CS492 Algorithms for NP-hard Problems. Spring 2024.
MilesCranmer/PySR
High-Performance Symbolic Regression in Python and Julia
mateodd25/free-nets
Learn one, get them all for free
PerformanceEstimation/Performance-Estimation-Toolbox
Code of the Performance Estimation Toolbox (PESTO) whose aim is to ease the access to the PEP methodology for performing worst-case analyses of first-order methods in convex and nonconvex optimization. The numerical worst-case analyses from PEP can be performed just by writting the algorithms just as you would implement them.
automl/TransformersCanDoBayesianInference
Official Implementation of "Transformers Can Do Bayesian Inference", the PFN paper
namkoong-lab/dro
A package of distributionally robust optimization (DRO) methods. Implemented via cvxpy and PyTorch
mxgmn/MarkovJunior
Probabilistic language based on pattern matching and constraint propagation, 153 examples
hongseok-yang/CLT23
thudzj/NeuralEigenFunction
srush/GPU-Puzzles
Solve puzzles. Learn CUDA.
kozistr/pytorch_optimizer
optimizer & lr scheduler & loss function collections in PyTorch
uilab-kaist/cs575-ethics-spring-2023
pytorch/functorch
functorch is JAX-like composable function transforms for PyTorch.
huggingface/knockknock
🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code
jettify/pytorch-optimizer
torch-optimizer -- collection of optimizers for Pytorch
pyro-ppl/pyro
Deep universal probabilistic programming with Python and PyTorch
pyro-ppl/numpyro
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
bayesiains/nflows
Normalizing flows in PyTorch
VincentStimper/normalizing-flows
PyTorch implementation of normalizing flow models
rtqichen/torchdiffeq
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
mekty2012/Bayes_Adversarial
We study the effect of various BNNs on the adversarial loss.
py-why/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
nlee0212/RankingGlider
tum-pbs/pbdl-book
Welcome to the Physics-based Deep Learning Book (v0.2)
wesselb/stheno
Gaussian process modelling in Python
f-dangel/backpack
BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.
dfm/tinygp
The tiniest of Gaussian Process libraries
probabilistic-numerics/probnum
Probabilistic Numerics in Python.