Briefly introduce methods of explainable ai in LLM

(Master's Thesis) Alam, Mahbub Ul, From Speech to Image: A Novel Approach to Understand the Hidden Layer Mechanisms of Deep Neural Networks in Automatic Speech Recognition, Masterarbeit, Institut für Maschinelle Sprachverarbeitung, Universität Stuttgart, 2017. (https://www.ims.uni-stuttgart.de/en/research/publications/theses/)

Repository for the 'best student paper award' winning paper at the IEEE 35th International Symposium on Computer Based Medical Systems (CBMS 2022), Exploring LRP and Grad-CAM visualization to interpret multi-label-multi-class pathology prediction using chest radiography, Mahbub Ul Alam, Jón Rúnar Baldvinsson and Yuxia Wang. https://doi.org/10.11…

ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs

An attempt at implementing the Layerwise Relevance Propagation in a CUDA runtime

Simulate directional sound – deep neural network (DNN) – layer-wise relevance propagation (LRP)

Transfer Explainability via Layer-Wise Relevance Propagation Demo for AAAI

Explainability of Deep RL algorithms using graph networks and layer-wise relevance propagation.

This repository contains the code to generate the questionnaire that was conducted for the sake of our paper *Labarta et al.: Study on the Helpfulness of Explainable Artificial Intelligence (2024)* as well as the scripts for the analysis of the gathered survey results.

Using Explainable Artificial Intelligence (XAI) for sentiment analysis (NLP)

Repository for the journal article 'SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction'

Implementation of explainability algorithms (layer-wise relevance propagation, local interpretable model-agnostic explanations, gradient-weighted class activation mapping) on computer vision architectures to identify and explain regions of COVID 19 pneumonia in chest X-ray and CT scans.

Code used in paper 'Comprehensive social trait judgments from faces in autism spectrum disorder'

An XAI library that helps to explain AI models in a really quick & easy way

Cyber Security AI Dashboard

Explain Neural Networks using Layer-Wise Relevance Propagation and evaluate the explanations using Pixel-Flipping and Area Under the Curve.

[ECCV 2022: Oral] In this work, we discover that color is a crtical transferable forensic feature (T-FF) in universal detectors for detecting CNN-generated images.

Explainable AI in Julia.

eirspo official branch of odoo for better localization and openness

We predict religion from personal names only.