Predicting BCF values with explanations
-
Updated
Feb 13, 2023 - Python
Predicting BCF values with explanations
Gated-ViGAT. Code and data for our paper: N. Gkalelis, D. Daskalakis, V. Mezaris, "Gated-ViGAT: Efficient bottom-up event recognition and explanation using a new frame selection policy and gating mechanism", IEEE International Symposium on Multimedia (ISM), Naples, Italy, Dec. 2022.
Data and codes for the EMNLP 2023 paper 'LLMs – the Good, the Bad or the Indispensable?: A Use Case on Legal Statute Prediction and Legal Judgment Prediction on Indian Court Cases'
A method for conditional shapley value estimation, built off the shapr package: https://github.com/NorskRegnesentral/shapr/tree/master
Official Implementation of TMLR's paper: "TabCBM: Concept-based Interpretable Neural Networks for Tabular Data"
Timeseries classification code for detecting confusion and agent errors. Published in Transactions on Affective Computing..
Explanations gain more and more attention in the context of explainable systems. This repo contains research about different aspects of explanations and how to integrate them in an existing system.
Implementation of Concept-level Debugging of Part-Prototype Networks
End-to-end ML: Using ML to generate expected PPS, opportunity grade classification, and prescription analysis for basketball coaches.
In this repository you will fine explainability of machine learning models.
Getting the Anchors Explainer to work in Different Settings
A Python library for creating TOPSIS rankings and visualizing the alternatives in WMSD for interpretation and designing improvement actions
code for paper in NLDB'23
Benchmark to Evaluate EXplainable AI
AI Explanation methods based on coalitional game theory, to explain any machine learning prediction.
Extrapolation, using XGBoost, of the high-resolution seismic echo-classification into a bigger area (bathymetric surface along SBP data).
Collection of ML / DL assignments completed in my AI and Healthcare Course at UW
App for deriving local / global explanations of UMAP embeddings based on principal component analysis (PCA).
Codebase for the paper "Generalized Reasoning with Graph Neural Networks by Relational Bayesian Network Encodings"
Add a description, image, and links to the explainability topic page so that developers can more easily learn about it.
To associate your repository with the explainability topic, visit your repo's landing page and select "manage topics."