Conway's Game of Life is sequential, here high-dimensional states are projected into the two-dimensional space, and connected, furthermore, meta-data is added to create interactive 2D visualizations.
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Updated
Nov 10, 2023 - HTML
Conway's Game of Life is sequential, here high-dimensional states are projected into the two-dimensional space, and connected, furthermore, meta-data is added to create interactive 2D visualizations.
A visual debugging tool for fine-tuned BERT models for (multilabel) sequence classification tasks
Interpreting Categorical Data Classifiers using Explanation-based Locality
Graduate research project in computer vision and deep learning explainability
GENIA: Study of gender biases in machine learning models using explainable artificial intelligence
Human vs AI: Frontend📱
Interpretable Transformer for Fine-grained Image Classification
Wine quality multi-class prediction neural net model implemented using pytorch with model exploration and explanation using shap.
This repo holds my attempt to explain fake news detection models.
Kaggle Machine Learning Courses Exercises
Slot Attention-based Classifier for Explainable Image Recognition
Comparison of sentiment analysis conducted with a lexicon and rule-based dictionary and state-of-the-art pre-trained language models
Fundamentals of Interpretable Data Science
GitHub repository for our work "Interpretable Machine Learning for Precision Aging"
Classifying Travel Mode choice in the Netherlands using KNN, XGBoost, RF and TabNet
Code, model and data for our paper: K. Tsigos, E. Apostolidis, S. Baxevanakis, S. Papadopoulos, V. Mezaris, "Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection", Proc. ACM Int. Workshop on Multimedia AI against Disinformation (MAD’24) at the ACM Int. Conf. on Multimedia Retrieval (ICMR’24), Thailand, June 2024.
This repository includes a machine learning modeling study about estimating customers hotel cancellation and what are the reasons for these cancellations.
This repository contains code, information and datasets for the project on making interpretable models titled "Model Agnostic Methods for Interpretable Machine Learning". The abstract can be accessed at https://docs.google.com/document/d/1k2-beHD4YQxXpH8ExUM2Gd-yE5VqdluhiCsUIO3czRM/edit?usp=sharing
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