Kejie Jiang's repositories
accelerated_sampling_with_autoencoder
Accelerated sampling framework with autoencoder-based method
adversarial
Code and hyperparameters for the paper "Generative Adversarial Networks"
Adversarial_Autoencoder
A wizard's guide to Adversarial Autoencoders
auto-sklearn
Automated Machine Learning with scikit-learn
combo
A Python Toolbox for Machine Learning Model Combination
Deep_Metric
Deep Metric Learning
deepAD
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the KDD'19 Workshop on Anomaly Detection in Finance that will walk you through the detection of interpretable accounting anomalies using adversarial autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.
DeePyMoD
DeepMod is a deep learning based model discovery algorithm which seeks the partial differential equation underlying a spatio-temporal data set. DeepMoD employs sparse regression on a library of basis functions and their corresponding spatial derivatives. This code is based on the paper: [arXiv:1904.09406](http://arxiv.org/abs/1904.09406)
graph_comb_opt
Implementation of "Learning Combinatorial Optimization Algorithms over Graphs"
hspace
Package for efficient calculation of multivariate joint entropy measures
JULE.torch
Torch code for our CVPR 2016 paper "Joint Unsupervised LEarning of Deep Representations and Image Clusters"
lihang-code
《统计学习方法》的代码实现
LNPR
completed codes of "lecture notes of probabilistic robotics"
Master-Thesis-BayesianCNN
Master Thesis on Bayesian Convolutional Neural Network using Variational Inference
numpy-ml
Machine learning, in numpy
openTSNE
Extensible, parallel implementations of t-SNE
Parametric-t-SNE
Running parametric t-SNE by Laurens Van Der Maaten with Octave and oct2py.
parametric_tSNE
parametric tSNE for eq4all finger spell recognition
pgmpy
Python Library for Probabilistic Graphical Models
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Probabilistic-Robotics-1
Probabilistic Robotics
Probabilistic-Robotics-2
Solution to programming exercises in the book Probabilistic Robotics, Intro to Autonomous Mobile Robot on Edx.org, and Robot Mapping taught in University of Freiburg (http://ais.informatik.uni-freiburg.de/teaching/ws15/mapping/)
pt-dec
PyTorch implementation of DEC (Deep Embedding Clustering)
pycma
Python implementation of CMA-ES
pyprobml
Python code for "Machine learning: a probabilistic perspective"
PyTorch-BayesianCNN
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
pytorch-pixelshuffle1d
1D version of Pytorch's PixelShuffle module
pytorch-semseg
Semantic Segmentation Architectures Implemented in PyTorch
torchdiffeq
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
vae
a simple vae and cvae from keras