40.2k citations · 41.2k across the 11 of their papers we have counts for
15 papers
End-to-end Risk Prediction of Atrial Fibrillation from the 12-Lead ECG by Deep Neural Networks
Theogene Habineza, Antônio H. Ribeiro, Daniel Gedon +3
Background: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of…
Invertible Kernel PCA with Random Fourier Features
Daniel Gedon, Antôni H. Ribeiro, Niklas Wahlström +1
Kernel principal component analysis (kPCA) is a widely studied method to construct a low-dimensional data representation after a nonlinear transformation. The prevailing method to…
Deep networks for system identification: a Survey
Gianluigi Pillonetto, Aleksandr Aravkin, Daniel Gedon +3
Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results i…
Surprises in adversarially-trained linear regression
Antônio H. Ribeiro, Dave Zachariah, Thomas B. Schön
State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to de…
Overparameterized Linear Regression under Adversarial Attacks
Antônio H. Ribeiro, Thomas B. Schön
We study the error of linear regression in the face of adversarial attacks. In this framework, an adversary changes the input to the regression model in order to maximize the predi…
How Convolutional Neural Networks Deal with Aliasing
Antônio H. Ribeiro, Thomas B. Schön
The convolutional neural network (CNN) remains an essential tool in solving computer vision problems. Standard convolutional architectures consist of stacked layers of operations t…
Deep Energy-Based NARX Models
Johannes N. Hendriks, Fredrik K. Gustafsson, Antônio H. Ribeiro +2
This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data. In particular, our interest is in learning a conditional distributio…
Beyond Occam's Razor in System Identification: Double-Descent when Modeling Dynamics
Antônio H. Ribeiro, Johannes N. Hendriks, Adrian G. Wills +1
System identification aims to build models of dynamical systems from data. Traditionally, choosing the model requires the designer to balance between two goals of conflicting natur…
Deep Convolutional Networks in System Identification
Carl Andersson, Antônio H. Ribeiro, Koen Tiels +2
Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system…
SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant +32
SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release…
On the smoothness of nonlinear system identification
Antônio H. Ribeiro, Koen Tiels, Jack Umenberger +2
We shed new light on the \textit{smoothness} of optimization problems arising in prediction error parameter estimation of linear and nonlinear systems. We show that for regions of…
Automatic diagnosis of the 12-lead ECG using a deep neural network
Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão +9
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacke…
Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network
Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela Paixão +7
We present a model for predicting electrocardiogram (ECG) abnormalities in short-duration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology…
Lasso Regularization Paths for NARMAX Models via Coordinate Descent
Antônio H. Ribeiro, Luis A. Aguirre
We propose a new algorithm for estimating NARMAX models with regularization for models represented as a linear combination of basis functions. Due to the -norm penalty t…
"Parallel Training Considered Harmful?": Comparing series-parallel and parallel feedforward network training
Antônio H. Ribeiro, Luis A. Aguirre
Neural network models for dynamic systems can be trained either in parallel or in series-parallel configurations. Influenced by early arguments, several papers justify the choice o…