interpretability

There are 57 repositories under interpretability topic.

shap / shap
A game theoretic approach to explain the output of any machine learning model.
deep-learning explainability gradient-boosting interpretability machine-learning shap shapley
Language:Jupyter Notebook 24675
EthicalML / awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
awesome awesome-list data-mining deep-learning explainability interpretability large-scale-machine-learning large-scale-ml machine-learning machine-learning-operations ml-operations ml-ops mlops privacy-preserving privacy-preserving-machine-learning privacy-preserving-ml production-machine-learning production-ml responsible-ai
19577
jacobgil / pytorch-grad-cam
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
class-activation-maps computer-vision deep-learning explainable-ai explainable-ml grad-cam image-classification interpretability interpretable-ai interpretable-deep-learning machine-learning object-detection pytorch score-cam vision-transformers visualizations xai
Language:Python 12329
interpretml / interpret
Fit interpretable models. Explain blackbox machine learning.
ai artificial-intelligence bias blackbox differential-privacy explainability explainable-ai explainable-ml gradient-boosting iml interpretability interpretable-ai interpretable-machine-learning interpretable-ml interpretml machine-learning scikit-learn transparency xai
Language:C++ 6707
meta-pytorch / captum
Model interpretability and understanding for PyTorch
feature-attribution feature-importance interpretability interpretable-ai interpretable-ml
Language:Python 5457
tensorflow / lucid
A collection of infrastructure and tools for research in neural network interpretability.
colab interpretability jupyter-notebook machine-learning tensorflow visualization
Language:Jupyter Notebook 4700
jphall663 / awesome-machine-learning-interpretability
A curated list of awesome responsible machine learning resources.
ai-safety awesome awesome-list data-science explainable-ml fairness interpretability interpretable-ai interpretable-machine-learning interpretable-ml machine-learning machine-learning-interpretability privacy-enhancing-technologies privacy-preserving-machine-learning python r reliable-ai secure-ml transparency xai
3906
stellargraph / stellargraph
StellarGraph - Machine Learning on Graphs
graphs machine-learning machine-learning-algorithms graph-convolutional-networks networkx geometric-deep-learning saliency-map interpretability heterogeneous-networks graph-neural-networks graph-analysis graph-machine-learning gcn link-prediction stellargraph-library graph-data python deep-learning data-science
Language:Python 3032
MAIF / shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
ethical-artificial-intelligence explainability explainable-ml interpretability lime machine-learning python shap transparency
Language:Jupyter Notebook 2971
SeldonIO / alibi
Algorithms for explaining machine learning models
counterfactual explanations interpretability machine-learning xai
Language:Python 2574
torch-cam
frgfm / torch-cam
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
activation-maps class-activation-map cnn deep-learning grad-cam gradcam gradcam-plus-plus interpretability interpretable-deep-learning python pytorch saliency-map score-cam smoothgrad
Language:Python 2264
chaoyanghe / Awesome-Federated-Learning
FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
adversarial-attack-and-defense communication-efficiency computation-efficiency computer-vision continual-learning decentralized-federated-learning distributed-optimization federated-learning hierarchical-federated-learning incentive-mechanism interpretability machine-learning neural-architecture-search non-iid privacy semi-supervised-learning straggler-problem transfer-learning vertical-federated-learning wireless-communication
1988
google-deepmind / penzai
A JAX research toolkit for building, editing, and visualizing neural networks.
fine-tuning interpretability jax neural-networks visualization
Language:Python 1824
microsoft / responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
data-analysis data-science data-visualization error-analysis explainability explainable-ai explainable-ml fairness fairness-ai fairness-ml interpretability jupyter machine-learning machinelearning ml responsible-ai ui visualization widget widgets
Language:TypeScript 1654
ramprs / grad-cam
[ICCV 2017] Torch code for Grad-CAM
convolutional-neural-networks deep-learning grad-cam heatmap iccv17 interpretability visual-explanation
Language:Lua 1602
wangyongjie-ntu / Awesome-explainable-AI
A collection of research materials on explainable AI/ML
counterfactual-explanations explainable-ai explanation-system interpretability interpretable-ai recourse xai xml
Language:Markdown 1577
stanfordnlp / pyreft
Stanford NLP Python library for Representation Finetuning (ReFT)
interpretability reft representation-finetuning
Language:Python 1526
imodels
csinva / imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
interpretability machine-learning data-science artificial-intelligence ml ai statistics scikit-learn python optimal-classification-tree rulefit imodels rule-learning supervised-learning explainable-ml explainable-ai rules bayesian-rule-list
Language:Jupyter Notebook 1509
DALEX
ModelOriented / DALEX
moDel Agnostic Language for Exploration and eXplanation (JMLR 2018; JMLR 2021)
black-box dalex data-science explainable-ai explainable-artificial-intelligence explainable-ml explanations explanatory-model-analysis fairness iml interpretability interpretable-machine-learning machine-learning model-visualization predictive-modeling responsible-ai responsible-ml xai
Language:Python 1445
cdpierse / transformers-interpret
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
nlp machine-learning natural-language-processing explainable-ai transformers model-explainability transformers-model captum deep-learning neural-network interpretability computer-vision
Language:Jupyter Notebook 1391
EthicalML / xai
XAI - An eXplainability toolbox for machine learning
ai artificial-intelligence bias bias-evaluation downsampling evaluation explainability explainable-ai explainable-ml feature-importance imbalance interpretability machine-learning machine-learning-explainability ml upsampling xai xai-library
Language:Python 1204
sicara / tf-explain
Interpretability Methods for tf.keras models with Tensorflow 2.x
deep-learning interpretability keras machine-learning tensorflow tf2 visualization
Language:Python 1035
hila-chefer / Transformer-MM-Explainability
[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
transformers transformer vqa detr visualization explainability explainable-ai interpretability lxmert visualbert clip
Language:Jupyter Notebook 873
kundajelab / deeplift
Public facing deeplift repo
deeplift sensitivity-analysis integrated-gradients guided-backpropagation saliency-map interpretable-deep-learning interpretability
Language:Python 868
shubhomoydas / ad_examples
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
ensemble-learning active-learning anomaly-detection rnn lstm explaination interpretability time-series timeseries trees unsuperivsed autoencoder streaming concept-drift generative-adversarial-network gan anogan graph-convolutional-networks adversarial-attacks nettack
Language:Python 865
pbiecek / xai_resources
Interesting resources related to XAI (Explainable Artificial Intelligence)
interpretability interpretable-machine-learning xai
Language:R 840
pyvene
stanfordnlp / pyvene
Stanford NLP Python library for understanding and improving PyTorch models via interventions
activation-intervention activation-patching interpretability intervention mechanistic-interpretability
Language:Python 826
oneTaken / awesome_deep_learning_interpretability
深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)
awesome awesome-list chainer computer-vision cvpr deep-learning eccv iccv iclr icml interpretability keras matlab neural-network neurips nlp papers pytorch tensorflow torch
760
MisaOgura / flashtorch
Visualization toolkit for neural networks in PyTorch! Demo -->
cnn deep-learning explainability interpretability machine-learning neural-networks pytorch visualization
Language:HTML 744
xplique
deel-ai / xplique
👋 Xplique is a Neural Networks Explainability Toolbox
xai explainable-ai explainable-ml interpretability
Language:Python 712
ndif-team / nnsight
The nnsight package enables interpreting and manipulating the internals of deep learned models.
interpretability machine-learning neural-networks python pytorch
Language:Jupyter Notebook 695
tensorflow / decision-forests
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
decision-forest decision-trees gradient-boosting interpretability keras machine-learning ml python random-forest tensorflow
Language:Python 690
kmeng01 / rome
Locating and editing factual associations in GPT (NeurIPS 2022)
transformers gpt interpretability pytorch
Language:Python 685
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.
machine-learning python fatml xai gradient-boosting-machine decision-tree data-science fairness interpretable-machine-learning interpretability machine-learning-interpretability iml accountability transparency data-mining interpretable-ml interpretable interpretable-ai lime h2o
Language:Jupyter Notebook 681
tensorflow / tcav
Code for the TCAV ML interpretability project
interpretability machine-learning tcav
Language:Jupyter Notebook 645
shapiq
mmschlk / shapiq
Shapley Interactions and Shapley Values for Machine Learning
banzhaf-index explainability explainable-ai feature-attribution feature-importance feature-interactions game-theory interpretability interpretable-machine-learning machine-learning python shap shapiq shapley shapley-interactions
Language:Python 629