338 citations · 382 across the 14 of their papers we have counts for
5 papers · 1 filter
iLTM: Integrated Large Tabular Model
David Bonet, Marçal Comajoan Cara, Alvaro Calafell +2
Tabular data underpins decisions across science, industry, and public services. Despite rapid progress, advances in deep learning have not fully carried over to the tabular domain,…
Compressive Meta-Learning
Daniel Mas Montserrat, David Bonet, Maria Perera +2
The rapid expansion in the size of new datasets has created a need for fast and efficient parameter-learning techniques. Compressive learning is a framework that enables efficient…
Feature Shift Localization Network
Míriam Barrabés, Daniel Mas Montserrat, Kapal Dev +1
Feature shifts between data sources are present in many applications involving healthcare, biomedical, socioeconomic, financial, survey, and multi-sensor data, among others, where…
HyperFast: Instant Classification for Tabular Data
David Bonet, Daniel Mas Montserrat, Xavier Giró-i-Nieto +1
Training deep learning models and performing hyperparameter tuning can be computationally demanding and time-consuming. Meanwhile, traditional machine learning methods like gradien…
Adversarial Learning for Feature Shift Detection and Correction
Miriam Barrabes, Daniel Mas Montserrat, Margarita Geleta +2
Data shift is a phenomenon present in many real-world applications, and while there are multiple methods attempting to detect shifts, the task of localizing and correcting the feat…