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Search results for explainable ai explainability
explainability
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explainable-ai
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50 search results found
Interpret
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5,992
Fit interpretable models. Explain blackbox machine learning.
Responsible Ai Toolbox
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1,187
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.
Xai
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1,060
XAI - An eXplainability toolbox for machine learning
Transformer Mm Explainability
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490
[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.
Awesome Graph Explainability Papers
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486
Papers about explainability of GNNs
Explainx
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375
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @
[email protected]
Tf Keras Vis
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286
Neural network visualization toolkit for tf.keras
Adversarial Explainable Ai
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235
💡 Adversarial attacks on explanations and how to defend them
Carla
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216
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Whitebox
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184
[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes
Openxai
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178
OpenXAI : Towards a Transparent Evaluation of Model Explanations
Zennit
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151
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
Graphxai
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128
GraphXAI: Resource to support the development and evaluation of GNN explainers
Hierarchical Dnn Interpretations
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119
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
Sagemaker Explaining Credit Decisions
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87
Amazon SageMaker Solution for explaining credit decisions.
Rrl
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82
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"
Pytorch_explain
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80
PyTorch Explain: Logic Explained Networks in Python.
Deep Explanation Penalization
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74
Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584
Treeshap
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72
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Fat Forensics
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63
Modular Python Toolbox for Fairness, Accountability and Transparency Forensics
Awesome Shapley Value
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63
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Conceptor
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61
Official implementation of the paper The Hidden Language of Diffusion Models
S3bert
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61
Semantically Structured Sentence Embeddings
Cxplain
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59
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
Azimuth
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50
Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.
Prototree
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46
ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
Potato
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44
XAI based human-in-the-loop framework for automatic rule-learning.
Influenciae
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42
👋 Influenciae is a Tensorflow Toolbox for Influence Functions
Concept Based Xai
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37
Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
Harmonization
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34
👋 Aligning Human & Machine Vision using explainability
Iprompt
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33
Finding semantically meaningful and accurate prompts.
Cem
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26
Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off"
Te2rules
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25
Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.
Bioexp
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23
Explainability of Deep Learning Models
Artemis
⭐
19
A Python package with explanation methods for extraction of feature interactions from predictive models
Modeling Uncertainty Local Explainability
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16
Local explanations with uncertainty 💐!
Xai Scholar
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16
Cross-field empirical trends analysis of XAI literature
Lernd
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15
Lernd is ∂ILP (dILP) framework implementation based on Deepmind's paper Learning Explanatory Rules from Noisy Data.
Gebi
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15
GEBI: Global Explanations for Bias Identification. Open source code for discovering bias in data with skin lesion dataset
Teex
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14
A Toolbox for the Evaluation of machine learning Explanations
Icml 2023 Route Interpret Repeat
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12
Official repository of ICML 2023 paper: Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat
Mllp
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12
The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
Awesome Time Series Explainability
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11
A list of (post-hoc) XAI for time series
Pytolemaic
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10
Toolbox for analysis of model's quality and model's description. For further details see
Responsible Ai Workshop
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9
Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice
Explainable Models With Consistent Interpretations
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8
Official repository for the AAAI-21 paper 'Explainable Models with Consistent Interpretations'
Doxpy
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7
Replication package for the KNOSYS paper titled "An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability".
Timex
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6
Time series explainability via self-supervised model behavior consistency
Cwox
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5
A XAI Framework to provide Contrastive Whole-output Explanation for Image Classification.
Cf Shap
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5
Counterfactual SHAP: a framework for counterfactual feature importance
1-50 of 50 search results
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