As part of the Explainable AI Toolkit (XAITK), XAITK-Saliency is an open source, explainable AI framework for visual saliency algorithm interfaces and implementations, built for analytics and autonomy applications.
We introduce XBrainLab, an open-source user-friendly software, for accelerated interpretation of neural patterns from EEG data based on cutting-edge computational approach.
🎙️ Speak with AI - Run locally using Ollama, OpenAI or xAI - Speech uses XTTS, OpenAI or ElevenLabs
Toolkit for semi-automated modelcard creation for AI/ML models.
ANN-based anomaly detection for vehicle components using oscilloscope recordings.
XAI methods analysis for Parkinson's Detection from speech.
[ICML'24] Official PyTorch Implementation of TimeX++
[NeurIPS 2024] Official implementation of the paper "MambaLRP: Explaining Selective State Space Sequence Models".
This work explores the impact of contextual biases on feature attribution methods in object recognition models, highlighting the need for robust, context-aware interpretability techniques.
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
This code implements ProtoViT, a novel approach that combines Vision Transformers with prototype-based learning to create interpretable image classification models. Our implementation provides both high accuracy and explainability through learned prototypes.
Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off" and our NeurIPS 2023 paper "Learning to Receive Help: Intervention-Aware Concept Embedding Models"
An hybrid method for explaining LLM predictions for Entity Resolution tasks
Official code for TMLR2024 paper PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans | https://openreview.net/pdf?id=OcFjqiJ98b
ViRelAy is a visualization tool for the analysis of data as generated by CoRelAy.
Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.
A Explainable Artificial Intelligence tool focused in ensemble black box models, based in Item Response Theory, called eXirt.
[NeurIPS 2024] Mesomorphic Interpretable Networks for Tabular Data
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