3 citations · 5 across the 5 of their papers we have counts for
5 papers
FADEx: Feature Attribution and Distortion-based Explanation of Dimensionality Reduction
Lucas Greff Meneses, Evandro S. Ortigossa, Claudio Silva +1
Dimensionality Reduction (DR) is a fundamental tool for high-dimensional data exploration, reducing the complexity of latent spaces of machine learning models, and assisting in the…
MoHETS: Long-term Time Series Forecasting with Mixture-of-Heterogeneous-Experts
Evandro S. Ortigossa, Guy Lutsker, Eran Segal
Real-world multivariate time series can exhibit intricate multi-scale structures, including global trends, local periodicities, and non-stationary regimes, which makes long-horizon…
Seg-MoE: Multi-Resolution Segment-wise Mixture-of-Experts for Time Series Forecasting Transformers
Evandro S. Ortigossa, Eran Segal
Transformer-based models have recently made significant advances in accurate time-series forecasting, but even these architectures struggle to scale efficiently while capturing lon…
Time Series Information Visualization -- A Review of Approaches and Tools
Evandro S. Ortigossa, Fábio F. Dias, Diego C. Nascimento +1
Time series data are prevalent across various domains and often encompass large datasets containing multiple time-dependent features in each sample. Exploring time-varying data is…
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients
Evandro S. Ortigossa, Fábio F. Dias, Brian Barr +2
The development of machine learning applications has increased significantly in recent years, motivated by the remarkable ability of learning-powered systems to discover and genera…