yaolezju's Stars
christophM/interpretable-ml-book
Book about interpretable machine learning
jphall663/awesome-machine-learning-interpretability
A curated list of awesome responsible machine learning resources.
farizrahman4u/seq2seq
Sequence to Sequence Learning with Keras
fengdu78/WZU-machine-learning-course
温州大学《机器学习》课程资料（代码、课件等）
GMvandeVen/continual-learning
PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.
lopusz/awesome-interpretable-machine-learning
dswah/pyGAM
[HELP REQUESTED] Generalized Additive Models in Python
pbiecek/xai_resources
Interesting resources related to XAI (Explainable Artificial Intelligence)
ethanfetaya/NRI
Neural relational inference for interacting systems - pytorch
jphall663/interpretable_machine_learning_with_python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
xunzheng/notears
DAGs with NO TEARS: Continuous Optimization for Structure Learning
hustcxl/Deep-learning-in-PHM
Deep learning in PHM，Deep learning in fault diagnosis，Deep learning in remaining useful life prediction
kpchamp/SindyAutoencoders
fishmoon1234/DAG-GNN
kuc2477/pytorch-ewc
Unofficial PyTorch implementation of DeepMind's PNAS 2017 paper "Overcoming Catastrophic Forgetting"
srebuffi/iCaRL
RuiShu/nn-bayesian-optimization
We use a modified neural network instead of Gaussian process for Bayesian optimization.
GoudetOlivier/CGNN
Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"
ilyakava/sumproduct
Sum product algorithm - Belief propagation (message passing) for factor graphs
danbar/fglib
factor graph library
Rachnog/disentanglment
Experiments with beta-VAE to learn disentangled representations from the data
rdlester/pyfac
Python implementation of Sum-Product for Factor Graphs
AmanDaVinci/SENN
Self-Explaining Neural Networks
wwtmodels/Benchmark-Simulation-Models
leishu02/EMNLP2017_DOC
code for our EMNLP 2017 paper "DOC: Deep Open Classification of Text Documents"
lethaiq/GRACE_KDD20
GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction. Thai Le, Suhang Wang, Dongwon Lee. 26th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining (KDD)
sufeidechabei/graphnets_gcn
This is the gcn implication in graphnet.
dmelis/SENN
Self-Explaining Neural Networks
ZebinYang/exnn
Enhanced Explainable Neural Network
stefanoteso/calimocho
Explanatory Interactive Machine Learning with Self-explaining Neural Networks