HPAI-BSC

AtomDetection_ACSTEM

explainable-agents-with-humans

Fluid-Communities

Fluid Communities: A community detection algorithm

Focus-Metric

MAMe-baselines

Baseline experiments of the paper: "A Closer Look at Art Mediums: The MAMe Image Classification Dataset" using the MAMe dataset.

MetH-baselines

This repository contains the code used to perform baseline experiments on the MetH-datasets paper.

pgeon

prompt_engine

Evaluate your model using advanced prompt strategies

SuSy

SuSy is a Spatial-Based Synthetic Image Detection and Recognition Model, designed to detect synthetic images and attribute them to specific generative models. This repository provides the code and instructions to train and evaluate SuSy or your own model for synthetic image detection.

tl-tradeoff

Tormos, A., Garcia-Gasulla, D., Gimenez-Abalos, V., & Alvarez-Napagao, S. When & How to Transfer with Transfer Learning. In Has it Trained Yet? NeurIPS 2022 Workshop.

HPAI-BSC/Focus-Metric

HPAI-BSC/MAMe-baselines

Baseline experiments of the paper: "A Closer Look at Art Mediums: The MAMe Image Classification Dataset" using the MAMe dataset.

HPAI-BSC/Fluid-Communities

Fluid Communities: A community detection algorithm

HPAI-BSC/prompt_engine

Evaluate your model using advanced prompt strategies

HPAI-BSC/pgeon

HPAI-BSC/tl-tradeoff

Tormos, A., Garcia-Gasulla, D., Gimenez-Abalos, V., & Alvarez-Napagao, S. When & How to Transfer with Transfer Learning. In Has it Trained Yet? NeurIPS 2022 Workshop.

HPAI-BSC/MetH-baselines

This repository contains the code used to perform baseline experiments on the MetH-datasets paper.

HPAI-BSC/SuSy

SuSy is a Spatial-Based Synthetic Image Detection and Recognition Model, designed to detect synthetic images and attribute them to specific generative models. This repository provides the code and instructions to train and evaluate SuSy or your own model for synthetic image detection.

HPAI-BSC/explainable-agents-with-humans

HPAI-BSC/AtomDetection_ACSTEM

HPAI-BSC/chromoplexy_analysis

Collaboration project with Life Sciences for finding chromoplexy patterns in genomes of cancer patiens

HPAI-BSC/interpretable_models_seminar

Seminar about interpretable machine learning models. It includes Generalized Linear Models and EBMs

HPAI-BSC/lm-evaluation-harness-medical-specialities

HPAI-BSC/medical-specialities

Code to classify question-answering datasets into some predefined categories

HPAI-BSC/value-based-water-consumption

HPAI-BSC/ephemerality

This project computes the ephemerality measure for twitter discussions.

HPAI-BSC/neural_patterns_abstractions

HPAI-BSC/Attribution-Confusion-Matrix

HPAI-BSC/basic-fne

HPAI-BSC/covid-19-charts

HPAI-BSC/fne

HPAI-BSC/foundation-model-transparency-index_template

Template for writting a thorough model card following the categories identified by Stanford's Foundation Model Transparency Index

HPAI-BSC/hpai-bsc.github.io

HPAI-BSC/igraph

Library for the analysis of networks

HPAI-BSC/networkx

Official NetworkX source code repository.

HPAI-BSC/python-igraph

Python interface for igraph

HPAI-BSC/Quantus

Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations