Topic: explainability Goto Github
Some thing interesting about explainability
Some thing interesting about explainability
explainability,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"
User: 12wang3
explainability,OpenXAI : Towards a Transparent Evaluation of Model Explanations
Organization: ai4life-group
Home Page: https://open-xai.github.io/
explainability,Holds code for our CVPR'23 tutorial: All Things ViTs: Understanding and Interpreting Attention in Vision.
Organization: all-things-vits
Home Page: https://all-things-vits.github.io/atv
explainability,Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
User: alvinwan
Home Page: https://nbdt.aaalv.in
explainability,Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Organization: astrazeneca
explainability,Amazon SageMaker Solution for explaining credit decisions.
Organization: awslabs
explainability,Explainability techniques for Graph Networks, applied to a synthetic dataset and an organic chemistry task. Code for the workshop paper "Explainability Techniques for Graph Convolutional Networks" (ICML19)
User: baldassarrefe
explainability,CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Organization: carla-recourse
explainability,🗺️ Data Cleaning and Textual Data Visualization 🗺️
User: charlesdedampierre
Home Page: https://charlesdedampierre.github.io/BunkaTopics/index.html
explainability,Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
User: chr5tphr
explainability,Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
User: csinva
Home Page: https://arxiv.org/abs/1806.05337
explainability,Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
User: csinva
Home Page: https://csinva.io/imodelsX/
explainability,Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
User: d909b
explainability,Collection of NLP model explanations and accompanying analysis tools
Organization: dfki-nlp
explainability,Visualization tool for Graph Neural Networks
Organization: dmlc
explainability,TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
User: dylan-slack
Home Page: https://nlp.ics.uci.edu/talk-to-healthcare-model/
explainability,STAGIN: Spatio-Temporal Attention Graph Isomorphism Network
User: egyptdj
Home Page: https://arxiv.org/abs/2105.13495
explainability,A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Organization: ethicalml
Home Page: https://ethical.institute/principles.html
explainability,XAI - An eXplainability toolbox for machine learning
Organization: ethicalml
Home Page: https://ethical.institute/principles.html#commitment-3
explainability,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]
Organization: explainx
Home Page: https://www.explainx.ai
explainability,Modular Python Toolbox for Fairness, Accountability and Transparency Forensics
Organization: fat-forensics
Home Page: https://fat-forensics.org
explainability,TimeSHAP explains Recurrent Neural Network predictions.
Organization: feedzai
explainability,Papers about explainability of GNNs
User: flyingdoog
explainability,Power Tools for AI Engineers With Deadlines
Organization: h1st-ai
Home Page: https://h1st.ai
explainability,Official implementation of Score-CAM in PyTorch
User: haofanwang
explainability,Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.
Organization: hate-alert
explainability,💡 Adversarial attacks on explanations and how to defend them
User: hbaniecki
Home Page: https://doi.org/10.1016/j.inffus.2024.102303
explainability,[NeurIPS 2022] Official PyTorch implementation of Optimizing Relevance Maps of Vision Transformers Improves Robustness. This code allows to finetune the explainability maps of Vision Transformers to enhance robustness.
User: hila-chefer
explainability,[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
User: hila-chefer
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.
User: hila-chefer
explainability,For calculating global feature importance using Shapley values.
User: iancovert
explainability,Fit interpretable models. Explain blackbox machine learning.
Organization: interpretml
Home Page: https://interpret.ml/docs
explainability,Neural network visualization toolkit for tf.keras
User: keisen
Home Page: https://keisen.github.io/tf-keras-vis-docs/
explainability,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
User: laura-rieger
explainability,ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
User: m-nauta
explainability,🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Organization: maif
Home Page: https://maif.github.io/shapash/
explainability,P-NET, Biologically informed deep neural network for prostate cancer classification and discovery
User: marakeby
explainability,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.
Organization: microsoft
Home Page: https://responsibleaitoolbox.ai/
explainability,GraphXAI: Resource to support the development and evaluation of GNN explainers
Organization: mims-harvard
Home Page: https://zitniklab.hms.harvard.edu/projects/GraphXAI
explainability,Visualization toolkit for neural networks in PyTorch! Demo -->
User: misaogura
Home Page: https://youtu.be/18Iw4qYqfPo
explainability,Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Organization: modeloriented
Home Page: https://modeloriented.github.io/treeshap/
explainability,PyTorch Explain: Interpretable Deep Learning in Python.
User: pietrobarbiero
explainability,Evaluating ChatGPT’s Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness
Organization: pkuserc
explainability,ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based models.
User: salimamoukou
explainability,Contextual AI adds explainability to different stages of machine learning pipelines - data, training, and inference - thereby addressing the trust gap between such ML systems and their users. It does not refer to a specific algorithm or ML method — instead, it takes a human-centric view and approach to AI.
Organization: sap-archive
Home Page: https://contextual-ai.readthedocs.io/en/latest
explainability,Training & evaluation library for text-based neural re-ranking and dense retrieval models built with PyTorch
User: sebastian-hofstaetter
Home Page: https://neural-ir-explorer.ec.tuwien.ac.at/
explainability,A game theoretic approach to explain the output of any machine learning model.
Organization: shap
Home Page: https://shap.readthedocs.io
explainability,streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
User: snehankekre
Home Page: https://pypi.org/project/streamlit-shap/
explainability,[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes
Organization: squaredev-io
Home Page: https://squaredev.io/whitebox/
explainability,CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks
Organization: xmed-lab
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