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explainable-ai
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Predictive AI layer for existing databases.
mysql
machine-learning
tensorflow
clickhouse
ml
mariadb
pytorch
artificial-intelligence
machine-learning-api
hacktoberfest
ludwig
automl
explainable-ai
explainable-ml
xai
xai-library
ai-tables
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Feb 20, 2021 - Python
orihime-kitajima
commented
Jul 3, 2019
How to use Watcher / WatcherClient over tcp/ip network?
Watcher seems to ZMQ server, and WatcherClient is ZMQ Client, but there is no API/Interface to config server IP address.
Do I need to implement a class that inherits from WatcherClient?
moDel Agnostic Language for Exploration and eXplanation
black-box
data-science
machine-learning
predictive-modeling
fairness
interpretability
explainable-artificial-intelligence
explanations
explainable-ai
explainable-ml
xai
model-visualization
interpretable-machine-learning
iml
dalex
responsible-ai
responsible-ml
explanatory-model-analysis
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Feb 20, 2021 - Python
Interpretability and explainability of data and machine learning models
machine-learning
deep-learning
artificial-intelligence
ibm-research
explainable-ai
explainable-ml
xai
ibm-research-ai
codait
trusted-ai
trusted-ml
explainabil
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Dec 4, 2020 - Python
data-science
machine-learning
interpretable-ai
interpretable-ml
explainable-ai
xai
interpretable-machine-learning
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Updated
Jul 9, 2020 - Python
Code, exercises and tutorials of my personal blog ! 📝
python
machine-learning
statistics
dataviz
ai
deep-learning
tensorflow
example
keras
tutorials
pytorch
explainable-ai
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Feb 18, 2020 - Jupyter Notebook
XAI - An eXplainability toolbox for machine learning
machine-learning
ai
evaluation
ml
artificial-intelligence
upsampling
bias
interpretability
feature-importance
explainable-ai
explainable-ml
xai
imbalance
downsampling
explainability
bias-evaluation
machine-learning-explainability
xai-library
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Oct 5, 2019 - Python
Generate Diverse Counterfactual Explanations for any machine learning model.
machine-learning
deep-learning
explainable-ai
explainable-ml
xai
interpretable-machine-learning
counterfactual-explanations
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Feb 21, 2021 - Python
Leave One Feature Out Importance
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Updated
Jan 27, 2021 - Python
machine-learning
deep-learning
sentiment-analysis
tensorflow
transformers
interpretability
aspect-based-sentiment-analysis
explainable-ai
explainable-ml
distill
bert-embeddings
transformer-models
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Dec 14, 2020 - Python
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.
machine-learning
scikit-learn
transparency
blackbox
bias
interpretability
explainable-artificial-intelligence
interpretable-ai
explainable-ai
explainable-ml
xai
interpretable-machine-learning
machine-learning-interpretability
explainability
aws-sagemaker
explainx
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Feb 7, 2021 - Jupyter Notebook
Human-explainable AI.
python
data-science
machine-learning
statistics
simulation
model-selection
data-analytics
hyperparameter-tuning
interpretability
explainable-ai
shap-vector-decomposition
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Updated
Feb 19, 2021 - Jupyter Notebook
machine-learning
computer-vision
deep-learning
neural-network
explainable-ai
interpretable-machine-learning
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Updated
Mar 29, 2019 - Jupyter Notebook
machine-learning
predictive-modeling
interactive-visualizations
interpretability
explainable-artificial-intelligence
explainable-ai
explainable-ml
xai
model-visualization
interpretable-machine-learning
iml
explainability
explanatory-model-analysis
explainable-machine-learning
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Updated
Jan 20, 2021 - R
A collection of research materials on explainable AI/ML
xml
interpretability
explanation-system
interpretable-ai
explainable-ai
xai
counterfactual-explanations
recourse
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Feb 19, 2021
A repository for explaining feature attributions and feature interactions in deep neural networks.
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Oct 5, 2020 - Jupyter Notebook
Examples of Data Science projects and Artificial Intelligence use cases
python
data-science
machine-learning
natural-language-processing
reinforcement-learning
computer-vision
deep-learning
time-series
examples
regression
data-visualization
artificial-intelligence
classification
explainable-ai
explainable-ml
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Dec 21, 2020 - Jupyter Notebook
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" (ICLR 2019)
python
data-science
machine-learning
statistics
deep-neural-networks
ai
deep-learning
neural-network
jupyter-notebook
ml
pytorch
artificial-intelligence
convolutional-neural-networks
acd
interpretation
interpretability
feature-importance
explainable-ai
iclr2019
explainability
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Updated
Feb 14, 2021 - Jupyter Notebook
Code repository for "Interpretable and Accurate Fine-grained Recognition via Region Grouping", CVPR 2020 (Oral)
pytorch
celeba
interpretability
celeba-dataset
fine-grained-classification
explainable-ai
face-segmentation
pytorch-implementation
cub-dataset
part-based-models
weakly-supervised-segmentation
weakly-supervised-localization
cvpr2020
cvpr-2020
cvpr-oral
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Aug 27, 2020 - Python
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
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Aug 22, 2020 - Python
Pytorch implementation of "Explainable and Explicit Visual Reasoning over Scene Graphs "
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Mar 17, 2019 - Python
A data-driven approach to quantify the value of classifiers in a machine learning ensemble.
classifier
machine-learning
deep-learning
random-forest
ensemble
ensemble-learning
game-theory
voting-classifier
random-forest-classifier
explainable-ai
explainable-ml
weighted-voting-games
shapley
owen
shapley-value
game-theory-toolbox
voting-game
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Feb 1, 2021 - Python
This repository contains PyTorch implementation for the baseline models from the paper Utterance-level Dialogue Understanding: An Empirical Study
natural-language-processing
sentiment-analysis
dialogue
pytorch
lstm
pretrained-models
emotion-analysis
bert
dialogue-systems
conversational-agents
emotion-recognition
natural-language-understanding
conversational-ai
adversarial-attacks
explainable-ai
dialogue-act
emotion-recognition-in-conversation
bert-embeddings
dialogue-understanding
utterance-level-dialogue
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Nov 24, 2020 - Python
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
python
data-science
machine-learning
ai
deep-learning
neural-network
jupyter-notebook
ml
pytorch
artificial-intelligence
convolutional-neural-network
fairness
interpretability
cdep
feature-importance
recurrent-neural-network
interpretable-deep-learning
explainable-ai
explainability
fairness-ml
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Oct 5, 2020 - Jupyter Notebook
Explaining the output of machine learning models with more accurately estimated Shapley values
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Feb 17, 2021 - R
Detect model's attention
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Jul 2, 2020 - Jupyter Notebook
Explainability for Vision Transformers
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Dec 31, 2020 - Python
A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.
machine-learning
artificial-intelligence
mnist
pattern-recognition
bitwise-operators
frequent-pattern-mining
rule-based
explainable-ai
tsetlin-machine
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Aug 16, 2019 - C
This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
visualization
data-science
machine-learning
widget
ui
jupyter
widgets
ml
machinelearning
fairness
error-analysis
explainable-ai
explainable-ml
fairness-ai
explainability
fairness-ml
responsible-ai
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Updated
Feb 21, 2021 - TypeScript
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