4 papers
Physics-Informed Mixture Models and Surrogate Models for Precision Additive Manufacturing
Sebastian Basterrech, Shuo Shan, Debabrata Adhikari +1
In this study, we leverage a mixture model learning approach to identify defects in laser-based Additive Manufacturing (AM) processes. By incorporating physics based principles, we…
An AutoML Framework using AutoGluonTS for Forecasting Seasonal Extreme Temperatures
Pablo RodrÃguez-Bocca, Guillermo Pereira, Diego Kiedanski +3
In recent years, great progress has been made in the field of forecasting meteorological variables. Recently, deep learning architectures have made a major breakthrough in forecast…
Unsupervised Assessment of Landscape Shifts Based on Persistent Entropy and Topological Preservation
Sebastian Basterrech
In Continual Learning (CL) contexts, concept drift typically refers to the analysis of changes in data distribution. A drift in the input data can have negative consequences on a l…
A Self-Organizing Clustering System for Unsupervised Distribution Shift Detection
Sebastián Basterrech, Line Clemmensen, Gerardo Rubino
Modeling non-stationary data is a challenging problem in the field of continual learning, and data distribution shifts may result in negative consequences on the performance of a m…