Ilán F. Carretero Juchnowicz's Projects
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"
Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
Concept Bottleneck Models, ICML 2020
R Code for Paper "Inference for High Dimensional Censored Quantile Regression"
Code relating to the novel feature selection method based on explainable deep learning, Integradient Gradients Feature Selection (IGFS).
A new framework to transform any neural networks into an interpretable concept-bottleneck-model (CBM) without needing labeled concept data
LLM101n: Let's build a Storyteller
Code for the paper: Studying How to Efficiently and Effectively Guide Models with Explanations. ICCV 2023.
PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)
Deliverables for POSD subject.
Smoothed Censored Quantile Regression
R codes for high-dimensional survival screening