TrustyAI Explainability Toolkit
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explainableai
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Supporting models and data to doi 10.1021/acs.jcim.1c01163
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OrganismCore transforms reasoning into executable artifacts built on the Universal Reasoning Substrate (URS). Its purpose is to accelerate discovery by making reasoning itself a programmable, transmissible, and model-agnostic object.
agi openresearch explainableai symbolicai reproducibleai reasoningobjects composablereasoning universalreasoningsubstrate semanticonboarding machineagents automatedonboardingprocess
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This repository contains a code of Unfold and Conquer Attribution Guidance, which is presented in a conference of Association for the Advancement of Artificial Intelligence 2023.
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Ths repo has the list of Interesting Literature in the domain of XAI
explanation interpretability explainable-artificial-intelligence explainable-ai explainable-ml xai interpretable-machine-learning explainable-machine-learning explainableai
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Hacking a Neural Network to understand what concepts the network learns in order to solve a logic task.
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Skin cancer classification using Transfer Learning and explainable AI
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Methods to interpret machine learning models/black box which can help us understand how it’s making decisions.
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Detects Pneumonia using Chest X rays through Deep Learning models
image-processing pytorch deeplearning convolutional-neural-networks inceptionv3 imageclassification cnn-classification chexnet chexnet-model explainableai imagedataset
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See the world through the eyes of AI
opencv yolo deeplearning computervision smartglasses explainableai aivisualization interactiveai machineperception visionlayers educationalai aiexplained layers-of-vision ai-visual-layers see-like-ai deepvision-lens how-computers-see
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Attention-guided convolutional autoencoder for one-class anomaly detection and localization on CIFAR-10, using CBAM and reconstruction-based scoring.
datascience autoencoder machinelearning deeplearning anomalydetection computervision cbam explainableai aiprojects
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An AI-powered clinical assistant using Retrieval-Augmented Generation (RAG) on the MIMIC-IV DiReCT dataset. It retrieves relevant patient cases and generates diagnostic reasoning using LLMs. Built with Streamlit, Transformers, FAISS, and SentenceTransformers.
nlp machinelearning artificialintelligence faiss pythonprojects huggingface streamlit opensourceai explainableai healthcareai retrievalaugmentedgeneration clinicaldecisionsupport transformermodels biomedicalnlp medicalinformatics sentencetransformers aiforhealthcare
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A comprehensive comparative study of 10+ feature selection techniques (including RFE and SHAP) to optimize ML models. Achieved a 73% reduction in feature space while maintaining >96% accuracy, highlighting key trade-offs between performance efficiency and model interpretability for production environments.
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GUI-based ransomware attack detection using processor and disk I/O telemetry with CNN2D classification, SHAP explainability, adversarial robustness testing, and 10-fold stratified cross-validation.
python malware virtualization keras cnn cybersecurity ransomware deeplearning featureextraction explainableai cnn2d
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experimental setup to hook llms to a logic engine, for proof traces and financial math
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