tranbahien's Stars
lutzroeder/netron
Visualizer for neural network, deep learning and machine learning models
he-y/Awesome-Pruning
A curated list of neural network pruning resources.
n2cholas/awesome-jax
JAX - A curated list of resources https://github.com/google/jax
tdurieux/anonymous_github
Anonymous Github is a proxy server to support anonymous browsing of Github repositories for open-science code and data.
csyhhu/Awesome-Deep-Neural-Network-Compression
Summary, Code for Deep Neural Network Quantization
facebookresearch/NeuralCompression
A collection of tools for neural compression enthusiasts.
Tiiiger/QPyTorch
Low Precision Arithmetic Simulation in PyTorch
AaltoML/BayesNewton
Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.
TyXe-BDL/TyXe
apple/learning-subspaces
yang-song/score_flow
Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)
CW-Huang/sdeflow-light
A minimalist implementation of score-based diffusion model
wesselb/neuralprocesses
A framework for composing Neural Processes in Python
GentleZhu/Shift-Robust-GNNs
"Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')
ruqizhang/discrete-langevin
neale/HyperGAN
Generative Model for Neural Networks
akuhren/selective_gp
Code accompanying the paper "Probabilistic Selection of Inducing Points in Sparse Gaussian Processes".
hans66hsu/GATS
Source code of "What Makes Graph Neural Networks Miscalibrated?" (NeurIPS 2022)
ratschlab/repulsive_ensembles
Repo for our paper "Repulsive deep ensembles are Bayesian"
WayneDW/Bayesian-Sparse-Deep-Learning
Code for An Adaptive Empirical Bayesian Method for Sparse Deep Learning (NeurIPS'19)
thudzj/ELLA
Code for Accelerated Linearized Laplace Approximation for Bayesian Deep Learning (ELLA, NeurIPS 22')
ermongroup/SPN_Variational_Inference
PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020
HSG-AIML/NeurIPS_2022-Generative_Hyper_Representations
Code Repository for the NeurIPS 2022 paper: "Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights".
bsauty/longitudinal-VAEs
An interpretable progression model for high-dimensional neuroimaging data.
vicgalle/sgmcmc-force
Samplers from the paper "Stochastic Gradient MCMC with Repulsive Forces"
cagatayyildiz/neural-ode-tutorial
Neural ODE tutorial
yfelekis/GVI-posteriors-in-Probabilistic-Deep-Learning
saitcakmak/gp-sampling
j-wilson/GPflowSampling implemented on torch ecosystem
skezle/IBP_BNN
zmtomorrow/GeneralizationGapInAmortizedInference