1 citations · 1 across the 12 of their papers we have counts for
12 papers
Multi-Domain Clustering via Measure Quantization
Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante
Clustering is a fundamental task in data analysis, typically addressed through centroid-based methods such as K-means. In this work, we present a general framework for multi-domain…
DeFed-GMM-DaDiL: A Decentralized Federated Framework for Domain Adaptation
Rebecca Clain, Eduardo Fernandes Montesuma, Fred Ngole Mboula
Decentralized multi-source domain adaptation seeks to transfer knowledge from multiple heterogeneous and related source domains to an unlabeled target domain in a decentralized set…
Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering
Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante
Learning low-dimensional representations from multi-view relational data is challenging when underlying geometries differ across views. We propose Bary-GWMDS, a Gromov-Wasserstein-…
Structure-Preserving Multi-View Embedding Using Gromov-Wasserstein Optimal Transport
Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante
Multi-view data analysis seeks to integrate multiple representations of the same samples in order to recover a coherent low-dimensional structure. Classical approaches often rely o…
ReBaPL: Repulsive Bayesian Prompt Learning
Yassir Bendou, Omar Ezzahir, Eduardo Fernandes Montesuma +3
Prompt learning has emerged as an effective technique for fine-tuning large-scale foundation models for downstream tasks. However, conventional prompt learning methods are prone to…
Wasserstein Gradient Flows for Scalable and Regularized Barycenter Computation
Eduardo Fernandes Montesuma, Yassir Bendou, Mike Gartrell
Wasserstein barycenters provide a principled approach for aggregating probability measures, while preserving the geometry of their ambient space. Existing discrete methods are not…