29 citations · 58 across the 16 of their papers we have counts for
16 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 Rodríguez-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…
A Natural Gas Consumption Forecasting System for Continual Learning Scenarios based on Hoeffding Trees with Change Point Detection Mechanism
Radek Svoboda, Sebastian Basterrech, Jedrzej Kozal +2
Forecasting natural gas consumption, considering seasonality and trends, is crucial in planning its supply and consumption and optimizing the cost of obtaining it, mainly by indust…
Re-visiting Reservoir Computing architectures optimized by Evolutionary Algorithms
Sebastián Basterrech, Tarun Kumar Sharma
For many years, Evolutionary Algorithms (EAs) have been applied to improve Neural Networks (NNs) architectures. They have been used for solving different problems, such as training…
Tracking changes using Kullback-Leibler divergence for the continual learning
Sebastián Basterrech, Michal Woźniak
Recently, continual learning has received a lot of attention. One of the significant problems is the occurrence of \emph{concept drift}, which consists of changing probabilistic ch…
Evolutionary Echo State Network: evolving reservoirs in the Fourier space
Sebastian Basterrech, Gerardo Rubino
The Echo State Network (ESN) is a class of Recurrent Neural Network with a large number of hidden-hidden weights (in the so-called reservoir). Canonical ESN and its variations have…
Prediction of Facebook Post Metrics using Machine Learning
Emmanuel Sam, Sergey Yarushev, Sebastián Basterrech +1
In this short paper, we evaluate the performance of three well-known Machine Learning techniques for predicting the impact of a post in Facebook. Social medias have a huge influenc…
Empirical Analysis of the Necessary and Sufficient Conditions of the Echo State Property
Sebastián Basterrech
The Echo State Network (ESN) is a specific recurrent network, which has gained popularity during the last years. The model has a recurrent network named reservoir, that is fixed du…
A Tutorial about Random Neural Networks in Supervised Learning
Sebastián Basterrech, Gerardo Rubino
Random Neural Networks (RNNs) are a class of Neural Networks (NNs) that can also be seen as a specific type of queuing network. They have been successfully used in several domains…
Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome
Andrea Mesa, Sebastián Basterrech, Gustavo Guerberoff +1
The article presents an application of Hidden Markov Models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the Variant Surface Glyc…
An Empirical Study of the L2-Boost technique with Echo State Networks
Sebastián Basterrech
A particular case of Recurrent Neural Network (RNN) was introduced at the beginning of the 2000s under the name of Echo State Networks (ESNs). The ESN model overcomes the limitatio…
An Experimental Analysis of the Echo State Network Initialization Using the Particle Swarm Optimization
Sebastián Basterrech, Enrique Alba, Václav Snášel
This article introduces a robust hybrid method for solving supervised learning tasks, which uses the Echo State Network (ESN) model and the Particle Swarm Optimization (PSO) algori…
Single-sided Real-time PESQ Score Estimation
Sebastián Basterrech, Gerardo Rubino, Martín Varela
For several years now, the ITU-T's Perceptual Evaluation of Speech Quality (PESQ) has been the reference for objective speech quality assessment. It is widely deployed in commercia…
Echo State Queueing Network: a new reservoir computing learning tool
Sebastián Basterrech, Gerardo Rubino
In the last decade, a new computational paradigm was introduced in the field of Machine Learning, under the name of Reservoir Computing (RC). RC models are neural networks which a…