Interpretability for sequence generation models 🐛 🔍

Cyber Security AI Dashboard

Collection of associated files for my bachelor thesis

Based on the papers "Interpretability Beyond Feature Attribution: QuantitativeTestingwithConceptActivationVectors(TCAV)" and Captum's instantiation https://captum.ai/docs/captum_insights, we developed this frontend for the Captum project based on the streamlit framework.

XAI-Tris

Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

Deep Classiflie is a framework for developing ML models that bolster fact-checking efficiency. As a POC, the initial alpha release of Deep Classiflie generates/analyzes a model that continuously classifies a single individual's statements (Donald Trump) using a single ground truth labeling source (The Washington Post). For statements the model d…

Collection of NLP model explanations and accompanying analysis tools

Model interpretability for Explainable Artificial Intelligence

End-to-end toxic Russian comment classification

COVID-19 forecasting model for East Java cities using Joint Learning. My undergrad thesis.

Overview of different model interpretability libraries.

This repository contains the source code for Indoor Scene Detector, a full stack deep learning computer vision application.

Interpretability Metrics

Training a CNN to recognize the current Go position with photorealistic renders

Trained Neural Networks (LSTM, HybridCNN/LSTM, PyramidCNN, Transformers, etc.) & comparison for the task of Hate Speech Detection on the OLID Dataset (Tweets).

OdoriFy is an open-source tool with multiple prediction engines. This is the source code of the webserver.

A small repository to test Captum Explainable AI with a trained Flair transformers-based text classifier.

XAI Tutorial for the Explainable AI track in the ALPS winter school 2021