Antonio Horta Ribeiro

Human-Aligned Image Models Improve Visual Decoding from the Brain

Rajabi, Nona; Ribeiro, Antônio H.; Vasco, Miguel; Taleb, Farzaneh et al.

Part of Proceedings of the 42nd International Conference on Machine Learning, 2025

Efficient Optimization Algorithms for Linear Adversarial Training

Horta Ribeiro, Antônio; Schön, Thomas B.; Zachariah, Dave; Bach, Francis

Part of International Conference on Artificial Intelligence and Statistics, p. 1207-1215, 2025

Prediction of Atrial Fibrillation From the ECG in the Community Using Deep Learning: A Multinational Study

Brant, Luisa C.C.; Horta Ribeiro, Antônio; Eromosele, Oseiwe B.; Pinto-Filho, Marcelo M. et al.

Part of Circulation, 2025

• DOI for Prediction of Atrial Fibrillation From the ECG in the Community Using Deep Learning: A Multinational Study

Kernel Learning with Adversarial Features: Numerical Efficiency and Adaptive Regularization

Horta Ribeiro, Antônio; Vävinggren, David; Zachariah, Dave; Schön, Thomas B. et al.

Part of Advances Neural Information Processing Systems, 2025

Deep networks for system identification: A survey

Pillonetto, Gianluigi; Aravkin, Aleksandr; Gedon, Daniel; Ljung, Lennart et al.

Part of Automatica, 2025

• DOI for Deep networks for system identification: A survey
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No Double Descent in Principal Component Regression: A High-Dimensional Analysis

Gedon, Daniel; Horta Ribeiro, Antônio; Schön, Thomas B.

Part of International Conference on Machine Learning (ICML), p. 15271-15293, 2024

AI-ECG and prediction of new atrial fibrillation: when the heart tells the age

Horta Ribeiro, Antônio; Ribeiro, Antonio Luiz P

Part of European Heart Journal, p. 853-855, 2024

• DOI for AI-ECG and prediction of new atrial fibrillation: when the heart tells the age

Screening for Chagas disease from the electrocardiogram using a deep neural network

Jidling, Carl; Gedon, Daniel; Schön, Thomas B.; Oliveira, Claudia Di Lorenzo et al.

Part of PLoS Neglected Tropical Diseases, 2023

• DOI for Screening for Chagas disease from the electrocardiogram using a deep neural network
• Download full text (pdf) of Screening for Chagas disease from the electrocardiogram using a deep neural network

Invertible Kernel PCA With Random Fourier Features

Gedon, Daniel; Ribeiro, Antônio H.; Wahlström, Niklas; Schön, Thomas B.

Part of IEEE Signal Processing Letters, p. 563-567, 2023

• DOI for Invertible Kernel PCA With Random Fourier Features

Overparameterized Linear Regression Under Adversarial Attacks

Horta Ribeiro, Antônio; Schön, Thomas B.

Part of IEEE Transactions on Signal Processing, p. 601-614, 2023

• DOI for Overparameterized Linear Regression Under Adversarial Attacks

A multi-source domain fine-tuning framework for deep generalization performance in physiological time series analysis

Zvuloni, Eran; Gagliardi, Guido; Horta Ribeiro, Antônio; Ribeiro, Antonio Luiz Pinho et al.

Part of Machine Learning: Health, 2026

• DOI for A multi-source domain fine-tuning framework for deep generalization performance in physiological time series analysis
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Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals

Gu, Xiao; Tang, Wei; Han, Jinpei; Sangha, Veer et al.

Part of Nature Machine Intelligence, p. 220-233, 2026

• DOI for Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals
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Human-Aligned Image Models Improve Visual Decoding from the Brain

Rajabi, Nona; Ribeiro, Antônio H.; Vasco, Miguel; Taleb, Farzaneh et al.

Part of Proceedings of the 42nd International Conference on Machine Learning, 2025

Deep Learning Amplified Early Stopping Bias: Overestimating Performance on Small Datasets

Rajabi, Nona; Ribeiro, Antônio H.; Vasco, Miguel; Kragic, Danica

Part of ICASSP 2025, 2025

• DOI for Deep Learning Amplified Early Stopping Bias: Overestimating Performance on Small Datasets

Efficient Optimization Algorithms for Linear Adversarial Training

Horta Ribeiro, Antônio; Schön, Thomas B.; Zachariah, Dave; Bach, Francis

Part of International Conference on Artificial Intelligence and Statistics, p. 1207-1215, 2025

A multi-source domain fine-tuning framework for deep generalization performance in physiological time series analysis

Zvuloni, Eran; Gagliardi, Guido; Horta Ribeiro, Antônio; Ribeiro, Antonio Luiz Pinho et al.

Part of Machine Learning: Health, 2026

• DOI for A multi-source domain fine-tuning framework for deep generalization performance in physiological time series analysis
• Download full text (pdf) of A multi-source domain fine-tuning framework for deep generalization performance in physiological time series analysis

Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals

Gu, Xiao; Tang, Wei; Han, Jinpei; Sangha, Veer et al.

Part of Nature Machine Intelligence, p. 220-233, 2026

• DOI for Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals
• Download full text (pdf) of Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals

Prediction of Atrial Fibrillation From the ECG in the Community Using Deep Learning: A Multinational Study

Brant, Luisa C.C.; Horta Ribeiro, Antônio; Eromosele, Oseiwe B.; Pinto-Filho, Marcelo M. et al.

Part of Circulation, 2025

• DOI for Prediction of Atrial Fibrillation From the ECG in the Community Using Deep Learning: A Multinational Study

Evaluation of AI ECG age in the prediction of cardiovascular diseases and risk factors: Exploratory data analysis

Sumwiza, Kellen; Horta Ribeiro, Antônio; Rushingabigwi, Gerard; Bakunzibake, Pierre et al.

Part of Journal of Electrocardiology, 2025

• DOI for Evaluation of AI ECG age in the prediction of cardiovascular diseases and risk factors: Exploratory data analysis

Kernel Learning with Adversarial Features: Numerical Efficiency and Adaptive Regularization

Horta Ribeiro, Antônio; Vävinggren, David; Zachariah, Dave; Schön, Thomas B. et al.

Part of Advances Neural Information Processing Systems, 2025

Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025

Reyna, Matthew A.; Koscova, Zuzana; Pavlus, Jan; Weigle, James et al.

Part of Computers in Cardiology (CinC), 2025

• DOI for Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025

The cost of explainability in artificial intelligence-enhanced electrocardiogram models

Patlatzoglou, Konstantinos; Pastika, Libor; Barker, Joseph; Sieliwonczyk, Ewa et al.

Part of npj Digital Medicine, 2025

• DOI for The cost of explainability in artificial intelligence-enhanced electrocardiogram models
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Exploring the feasibility of olfactory brain-computer interfaces

Rajabi, Nona; Zanettin, Irene; Ribeiro, Antônio H.; Vasco, Miguel et al.

Part of Scientific Reports, 2025

• DOI for Exploring the feasibility of olfactory brain-computer interfaces
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Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study

Sau, Arunashis; Sieliwonczyk, Ewa; Patlatzoglou, Konstantinos; Pastika, Libor et al.

Part of The Lancet Digital Health, 2025

• DOI for Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study
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Explainable AI associates ECG aging effects with increased cardiovascular risk in a longitudinal population study

Hempel, Philip; Horta Ribeiro, Antônio; Vollmer, Marcus; Bender, Theresa et al.

Part of npj Digital Medicine, 2025

• DOI for Explainable AI associates ECG aging effects with increased cardiovascular risk in a longitudinal population study
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Deep networks for system identification: A survey

Pillonetto, Gianluigi; Aravkin, Aleksandr; Gedon, Daniel; Ljung, Lennart et al.

Part of Automatica, 2025

• DOI for Deep networks for system identification: A survey
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Genetic and phenotypic architecture of human myocardial trabeculation

McGurk, Kathryn A.; Qiao, Mengyun; Zheng, Sean L.; Sau, Arunashis et al.

Part of Nature Cardiovascular Research, p. 1503-1515, 2024

• DOI for Genetic and phenotypic architecture of human myocardial trabeculation
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Amplified Early Stopping Bias: Overestimated Performance with Deep Learning

Rajabi, Nona; Horta Ribeiro, Antônio; Vasco, Miguel; Kragic, Danica

Part of NeurIPS 2024 Workshop on Scientific Methods for Understanding Deep Learning, 2024

Transferability and Adversarial Training in Automatic Classification of the Electrocardiogram with Deep Learning

Eriksson, Arvid; Schön, Thomas B.; Horta Ribeiro, Antônio

Part of Computers in Cardiology (CinC), 2024

A comparison of artificial intelligence–enhanced electrocardiography approaches for the prediction of time to mortality using electrocardiogram images

Sau, Arunashis; Zeidaabadi, Boroumand; Patlatzoglou, Konstantinos; Pastika, Libor et al.

Part of The European Heart Journal - Digital Health, p. 180-189, 2024

• DOI for A comparison of artificial intelligence–enhanced electrocardiography approaches for the prediction of time to mortality using electrocardiogram images
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AI-ECG and prediction of new atrial fibrillation: when the heart tells the age

Horta Ribeiro, Antônio; Ribeiro, Antonio Luiz P

Part of European Heart Journal, p. 853-855, 2024

• DOI for AI-ECG and prediction of new atrial fibrillation: when the heart tells the age

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease

Pastika, Libor; Sau, Arunashis; Patlatzoglou, Konstantinos; Sieliwonczyk, Ewa et al.

Part of npj Digital Medicine, 2024

• DOI for Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease
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Prognostic Significance and Associations of Neural Network–Derived Electrocardiographic Features

Sau, Arunashis; Horta Ribeiro, Antônio; McGurk, Kathryn A.; Pastika, Libor et al.

Part of Circulation. Cardiovascular Quality and Outcomes, 2024

• DOI for Prognostic Significance and Associations of Neural Network–Derived Electrocardiographic Features
• Download full text (pdf) of Prognostic Significance and Associations of Neural Network–Derived Electrocardiographic Features

Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study

Sau, Arunashis; Pastika, Libor; Sieliwonczyk, Ewa; Patlatzoglou, Konstantinos et al.

Part of The Lancet Digital Health, 2024

• DOI for Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study
• Download full text (pdf) of Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study

Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification

Lu, Lei; Zhu, Tingting; Horta Ribeiro, Antônio; Clifton, Lei et al.

Part of The European Heart Journal - Digital Health, p. 247-259, 2024

• DOI for Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification
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Generalization challenges in electrocardiogram deep learning: insights from dataset characteristics and attention mechanism

Huang, Zhaojing; MacLachlan, Sarisha; Yu, Leping; Contreras, Luis Fernando Herbozo et al.

Part of Future Cardiology, p. 209-220, 2024

• DOI for Generalization challenges in electrocardiogram deep learning: insights from dataset characteristics and attention mechanism

Evaluating regression and probabilistic methods for ECG-based electrolyte prediction

von Bachmann, Philipp; Gedon, Daniel; Gustafsson, Fredrik K.; Ribeiro, Antônio H. et al.

Part of Scientific Reports, 2024

• DOI for Evaluating regression and probabilistic methods for ECG-based electrolyte prediction
• Download full text (pdf) of Evaluating regression and probabilistic methods for ECG-based electrolyte prediction

Neural Network-Derived Electrocardiographic Features Have Prognostic Significance and Important Phenotypic and Genotypic Associations

Sau, Arunashis; Horta Ribeiro, Antônio; McGurk, Kathryn; Pastika, Libor et al.

Part of European Heart Journal, 2023

Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival

Lindow, Thomas; Maanja, Maren; Schelbert, Erik B.; Horta Ribeiro, Antônio et al.

Part of The European Heart Journal - Digital Health, p. 384-392, 2023

• DOI for Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival
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Association of lifestyle with deep learning predicted electrocardiographic age

Zhang, Cuili; Miao, Xiao; Wang, Biqi; Thomas, Robert J. J. et al.

Part of Frontiers in Cardiovascular Medicine, 2023

• DOI for Association of lifestyle with deep learning predicted electrocardiographic age
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On Merging Feature Engineering and Deep Learning for Diagnosis, Risk Prediction and Age Estimation Based on the 12-Lead ECG

Zvuloni, Eran; Read, Jesse; Horta Ribeiro, Antônio; Ribeiro, Antonio Luiz P. et al.

Part of IEEE Transactions on Biomedical Engineering, p. 2227-2236, 2023

• DOI for On Merging Feature Engineering and Deep Learning for Diagnosis, Risk Prediction and Age Estimation Based on the 12-Lead ECG

Association Between Electrocardiographic Age and Cardiovascular Events in Community Settings: The Framingham Heart Study

Brant, Luisa C. C.; Horta Ribeiro, Antônio; Pinto-Filho, Marcelo M.; Kornej, Jelena et al.

Part of Circulation. Cardiovascular Quality and Outcomes, 2023

• DOI for Association Between Electrocardiographic Age and Cardiovascular Events in Community Settings: The Framingham Heart Study

Screening for Chagas disease from the electrocardiogram using a deep neural network

Jidling, Carl; Gedon, Daniel; Schön, Thomas B.; Oliveira, Claudia Di Lorenzo et al.

Part of PLoS Neglected Tropical Diseases, 2023

• DOI for Screening for Chagas disease from the electrocardiogram using a deep neural network
• Download full text (pdf) of Screening for Chagas disease from the electrocardiogram using a deep neural network

Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images

Sangha, Veer; Nargesi, Arash A.; Dhingra, Lovedeep S.; Khunte, Akshay et al.

Part of Circulation, p. 765-777, 2023

• DOI for Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images

End-to-end risk prediction of atrial fibrillation from the 12-Lead ECG by deep neural networks

Habineza, Theogene; Horta Ribeiro, Antônio; Gedon, Daniel; Behar, Joachim A. et al.

Part of Journal of Electrocardiology, p. 193-200, 2023

• DOI for End-to-end risk prediction of atrial fibrillation from the 12-Lead ECG by deep neural networks
• Download full text (pdf) of End-to-end risk prediction of atrial fibrillation from the 12-Lead ECG by deep neural networks

Invertible Kernel PCA With Random Fourier Features

Gedon, Daniel; Ribeiro, Antônio H.; Wahlström, Niklas; Schön, Thomas B.

Part of IEEE Signal Processing Letters, p. 563-567, 2023

• DOI for Invertible Kernel PCA With Random Fourier Features

Overparameterized Linear Regression Under Adversarial Attacks

Horta Ribeiro, Antônio; Schön, Thomas B.

Part of IEEE Transactions on Signal Processing, p. 601-614, 2023

• DOI for Overparameterized Linear Regression Under Adversarial Attacks

Knowledge Discovery with Electrocardiography Using Interpretable Deep Neural Networks

Lu, Lei; Zhu, Tingting; Horta Ribeiro, Antônio; Clifton, Lei et al.

Part of medRxiv, 2022

• DOI for Knowledge Discovery with Electrocardiography Using Interpretable Deep Neural Networks

Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients

Gustafsson, Stefan; Gedon, Daniel; Lampa, Erik; Horta Ribeiro, Antônio et al.

Part of Scientific Reports, 2022

• DOI for Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients
• Download full text (pdf) of Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients

Automated multilabel diagnosis on electrocardiographic images and signals

Sangha, Veer; Mortazavi, Bobak J.; Haimovich, Adrian D.; Horta Ribeiro, Antônio et al.

Part of Nature Communications, 2022

• DOI for Automated multilabel diagnosis on electrocardiographic images and signals
• Download full text (pdf) of Automated multilabel diagnosis on electrocardiographic images and signals

ResNet-based ECG Diagnosis of Myocardial Infarction in the Emergency Department

Gedon, Daniel; Gustafsson, Stefan; Lampa, Erik; Horta Ribeiro, Antônio et al.

Part of Machine learning from ground truth: New medical imaging datasets for unsolved medical problems Workshop at NeurIPS, 2021

Atrial fibrillation risk prediction from the 12-lead electrocardiogram using digital biomarkers and deep representation learning

Biton, Shany; Gendelman, Sheina; Horta Ribeiro, Antônio; Miana, Gabriela et al.

Part of The European Heart Journal - Digital Health, p. 576-585, 2021

• DOI for Atrial fibrillation risk prediction from the 12-lead electrocardiogram using digital biomarkers and deep representation learning
• Download full text (pdf) of Atrial fibrillation risk prediction from the 12-lead electrocardiogram using digital biomarkers and deep representation learning

Electrocardiographic Predictors of Mortality: Data from a Primary Care Tele-Electrocardiography Cohort of Brazilian Patients

Paixão, Gabriela M. M.; Lima, Emilly M.; Gomes, Paulo R.; Oliveira, Derick M. et al.

Part of Hearts, p. 449-458, 2021

• DOI for Electrocardiographic Predictors of Mortality: Data from a Primary Care Tele-Electrocardiography Cohort of Brazilian Patients

Deep neural network-estimated electrocardiographic age as a mortality predictor

Lima, Emilly M.; Horta Ribeiro, Antônio; Paixao, Gabriela M. M.; Horta Ribeiro, Manoel et al.

Part of Nature Communications, 2021

• DOI for Deep neural network-estimated electrocardiographic age as a mortality predictor
• Download full text (pdf) of Deep neural network-estimated electrocardiographic age as a mortality predictor

Contextualized Interpretable Machine Learning for Medical Diagnosis

Meira Jr, Wagner; Ribeiro, Antonio L. P.; Oliveira, Derick M.; Horta Ribeiro, Antônio

Part of Communications of the ACM, 2020

• DOI for Contextualized Interpretable Machine Learning for Medical Diagnosis

Evaluation of Mortality in Atrial Fibrillation: Clinical Outcomes in Digital Electrocardiography (CODE) Study

Paixão, Gabriela M. M.; Silva, Luis Gustavo S.; Gomes, Paulo R.; Lima, Emilly M. et al.

Part of Global Heart, 2020

• DOI for Evaluation of Mortality in Atrial Fibrillation: Clinical Outcomes in Digital Electrocardiography (CODE) Study

SciPy 1.0–Fundamental Algorithms for Scientific Computing in Python

Virtanen, Pauli; Gommers, Ralf; Oliphant, Travis E.; Haberland, Matt et al.

Part of Nature Methods, p. 261-272, 2020

• DOI for SciPy 1.0–Fundamental Algorithms for Scientific Computing in Python

Explaining End-to-End ECG Automated Diagnosis Using Contextual Features

Oliveira, Derick M.; Horta Ribeiro, Antônio; Pedrosa, João A. O.; Paixao, Gabriela M.M. et al.

Part of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2020

• DOI for Explaining End-to-End ECG Automated Diagnosis Using Contextual Features

Explaining Black-Box Automated Electrocardiogram Classification to Cardiologists

Oliveira, Derick M; Horta Ribeiro, Antônio; Pedrosa, Joao A O; Paixao, Gabriela M M et al.

Part of 2020 Computing in Cardiology (CinC), 2020

• DOI for Explaining Black-Box Automated Electrocardiogram Classification to Cardiologists

Automatic diagnosis of the 12-lead ECG using a deep neural network

Ribeiro, Antônio H.; Ribeiro, Manoel Horta; Paixao, Gabriela M. M.; Oliveira, Derick M. et al.

Part of Nature Communications, 2020

• DOI for Automatic diagnosis of the 12-lead ECG using a deep neural network
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On the smoothness of nonlinear system identification

Horta Ribeiro, Antônio; Tiels, Koen; Umenberger, Jack; Schön, Thomas B. et al.

Part of Automatica, 2020

• DOI for On the smoothness of nonlinear system identification

Tele-Electrocardiography and Bigdata: The CODE (Clinical Outcomes in Digital Electrocardiography) Study

Ribeiro, Antonio Luiz P.; Paixão, Gabriela M. M.; Gomes, Paulo R.; Ribeiro, Manoel Horta et al.

Part of Journal of Electrocardiology, 2019

• DOI for Tele-Electrocardiography and Bigdata: The CODE (Clinical Outcomes in Digital Electrocardiography) Study

Evaluation of Mortality in Bundle Branch Block Patients from an Electronic Cohort: Clinical Outcomes in Digital Electrocardiography (CODE) Study

Paixão, Gabriela M. M.; Lima, Emilly M.; Gomes, Paulo R.; Ferreira, Milton P. et al.

Part of Journal of Electrocardiology, 2019

• DOI for Evaluation of Mortality in Bundle Branch Block Patients from an Electronic Cohort: Clinical Outcomes in Digital Electrocardiography (CODE) Study

Clinical Outcomes in Digital Electrocardiography: Evaluation of Mortality in Atrial Fibrillation (Code Study)

Paixao, Gabriela; e Silva, Luis Gustavo Silva; Gomes, Paulo R.; Ferreira, Milton et al.

Part of Circulation. Abstracts from American Heart Association’s., 2018

Lasso Regularization Paths for NARMAX Models via Coordinate Descent

Horta Ribeiro, Antônio; Aguirre, Luis A.

Part of 2018 Annual American Control Conference (ACC), p. 5268-5273, 2018

• DOI for Lasso Regularization Paths for NARMAX Models via Coordinate Descent

”Parallel Training Considered Harmful?”: Comparing Series-Parallel and Parallel Feedforward Network Training

Horta Ribeiro, Antônio; Aguirre, Luis A.

Part of Neurocomputing, p. 222-231, 2018

• DOI for ”Parallel Training Considered Harmful?”: Comparing Series-Parallel and Parallel Feedforward Network Training

Shooting Methods for Parameter Estimation of Output Error Models

Horta Ribeiro, Antônio; Aguirre, Luis A.

Part of IFAC-PapersOnLine, p. 13998-14003, 2017

• DOI for Shooting Methods for Parameter Estimation of Output Error Models

Selecting Transients Automatically for the Identification of Models for an Oil Well

Horta Ribeiro, Antônio; Aguirre, Luis A.

Part of IFAC Workshop on Automatic Control in Offshore Oil and Gas Production, p. 154-158, 2015

• DOI for Selecting Transients Automatically for the Identification of Models for an Oil Well

Human-Aligned Image Models Improve Visual Decoding from the Brain

Rajabi, Nona; Ribeiro, Antônio H.; Vasco, Miguel; Taleb, Farzaneh et al.

Part of Proceedings of the 42nd International Conference on Machine Learning, 2025

Deep Learning Amplified Early Stopping Bias: Overestimating Performance on Small Datasets

Rajabi, Nona; Ribeiro, Antônio H.; Vasco, Miguel; Kragic, Danica

Part of ICASSP 2025, 2025

• DOI for Deep Learning Amplified Early Stopping Bias: Overestimating Performance on Small Datasets

Efficient Optimization Algorithms for Linear Adversarial Training

Horta Ribeiro, Antônio; Schön, Thomas B.; Zachariah, Dave; Bach, Francis

Part of International Conference on Artificial Intelligence and Statistics, p. 1207-1215, 2025

Contrasting by Augmented Patient Electrocardiograms to Learn Representations for a Foundation Model

Khattak, Gul Rukh; Patlatzoglou, Konstantinos; Liang, Yixiu; Pastika, Libor et al.

Part of Artificial Intelligence in Medicine, p. 202-206, 2025

• DOI for Contrasting by Augmented Patient Electrocardiograms to Learn Representations for a Foundation Model

No Double Descent in Principal Component Regression: A High-Dimensional Analysis

Gedon, Daniel; Horta Ribeiro, Antônio; Schön, Thomas B.

Part of International Conference on Machine Learning (ICML), p. 15271-15293, 2024

Can Transformers Smell Like Humans?

Taleb, Farzaneh; Vasco, Miguel; Horta Ribeiro, Antônio; Björkman, Mårten et al.

Part of Advances in Neural Information Processing Systems, 2024

Regularization properties of adversarially-trained linear regression

Horta Ribeiro, Antônio; Zachariah, Dave; Bach, Francis; Schön, Thomas B.

Part of Advances in Neural Information Processing Systems 36 (NeurIPS 2023), p. 23658-23670, 2023

• DOI for Regularization properties of adversarially-trained linear regression

First Steps Towards Self-Supervised Pretraining of the 12-Lead ECG

Gedon, Daniel; Horta Ribeiro, Antônio; Wahlström, Niklas; Schön, Thomas B.

Part of 2021 Computing In Cardiology (CINC), 2021

• DOI for First Steps Towards Self-Supervised Pretraining of the 12-Lead ECG

How convolutional neural networks deal with aliasing

Horta Ribeiro, Antônio; Schön, Thomas B.

Part of 2021 IEEE International Conference On Acoustics, Speech And Signal Processing (ICASSP 2021), p. 2755-2759, 2021

• DOI for How convolutional neural networks deal with aliasing

Beyond Exploding and Vanishing Gradients: Attractors and Smoothness in the Analysis of Recurrent Neural Network Training

Horta Ribeiro, Antônio; Tiels, Koen; Aguirre, Luis A.; Schön, Thomas B.

Part of Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, p. 2370-2380, 2020

Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble

Ribeiro, Antônio H.; Gedon, Daniel; Martins Teixeira, Daniel; Horta Ribeiro, Manoel et al.

Part of 2020 Computing in Cardiology, 2020

• DOI for Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble

Deep convolutional networks in system identification

Andersson, Carl; Horta Ribeiro, Antônio; Tiels, Koen; Wahlström, Niklas et al.

Part of Proc. 58th IEEE Conference on Decision and Control, p. 3670-3676, 2019

• DOI for Deep convolutional networks in system identification