Antonio Horta Ribeiro

Human-Aligned Image Models Improve Visual Decoding from the Brain

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

Ingår i 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

Ingår i International Conference on Artificial Intelligence and Statistics, s. 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.

Ingår i Circulation, 2025

• DOI för 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.

Ingår i Advances Neural Information Processing Systems, 2025

Deep networks for system identification: A survey

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

Ingår i Automatica, 2025

• DOI för Deep networks for system identification: A survey
• Ladda ner fulltext (pdf) av Deep networks for system identification: A survey

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

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

Ingår i International Conference on Machine Learning (ICML), s. 15271-15293, 2024

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

Horta Ribeiro, Antônio; Ribeiro, Antonio Luiz P

Ingår i European Heart Journal, s. 853-855, 2024

• DOI för 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.

Ingår i PLoS Neglected Tropical Diseases, 2023

• DOI för Screening for Chagas disease from the electrocardiogram using a deep neural network
• Ladda ner fulltext (pdf) av 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.

Ingår i IEEE Signal Processing Letters, s. 563-567, 2023

• DOI för Invertible Kernel PCA With Random Fourier Features

Overparameterized Linear Regression Under Adversarial Attacks

Horta Ribeiro, Antônio; Schön, Thomas B.

Ingår i IEEE Transactions on Signal Processing, s. 601-614, 2023

• DOI för 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.

Ingår i Machine Learning: Health, 2026

• DOI för A multi-source domain fine-tuning framework for deep generalization performance in physiological time series analysis
• Ladda ner fulltext (pdf) av 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.

Ingår i Nature Machine Intelligence, s. 220-233, 2026

• DOI för Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals
• Ladda ner fulltext (pdf) av Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals

Human-Aligned Image Models Improve Visual Decoding from the Brain

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

Ingår i 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

Ingår i ICASSP 2025, 2025

• DOI för 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

Ingår i International Conference on Artificial Intelligence and Statistics, s. 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.

Ingår i Machine Learning: Health, 2026

• DOI för A multi-source domain fine-tuning framework for deep generalization performance in physiological time series analysis
• Ladda ner fulltext (pdf) av 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.

Ingår i Nature Machine Intelligence, s. 220-233, 2026

• DOI för Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals
• Ladda ner fulltext (pdf) av 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.

Ingår i Circulation, 2025

• DOI för 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.

Ingår i Journal of Electrocardiology, 2025

• DOI för 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.

Ingår i 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.

Ingår i Computers in Cardiology (CinC), 2025

• DOI för 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.

Ingår i npj Digital Medicine, 2025

• DOI för The cost of explainability in artificial intelligence-enhanced electrocardiogram models
• Ladda ner fulltext (pdf) av The cost of explainability in artificial intelligence-enhanced electrocardiogram models

Exploring the feasibility of olfactory brain-computer interfaces

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

Ingår i Scientific Reports, 2025

• DOI för Exploring the feasibility of olfactory brain-computer interfaces
• Ladda ner fulltext (pdf) av Exploring the feasibility of olfactory brain-computer interfaces

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.

Ingår i The Lancet Digital Health, 2025

• DOI för Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study
• Ladda ner fulltext (pdf) av Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study

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.

Ingår i npj Digital Medicine, 2025

• DOI för Explainable AI associates ECG aging effects with increased cardiovascular risk in a longitudinal population study
• Ladda ner fulltext (pdf) av Explainable AI associates ECG aging effects with increased cardiovascular risk in a longitudinal population study

Deep networks for system identification: A survey

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

Ingår i Automatica, 2025

• DOI för Deep networks for system identification: A survey
• Ladda ner fulltext (pdf) av Deep networks for system identification: A survey

Genetic and phenotypic architecture of human myocardial trabeculation

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

Ingår i Nature Cardiovascular Research, s. 1503-1515, 2024

• DOI för Genetic and phenotypic architecture of human myocardial trabeculation
• Ladda ner fulltext (pdf) av Genetic and phenotypic architecture of human myocardial trabeculation

Amplified Early Stopping Bias: Overestimated Performance with Deep Learning

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

Ingår i 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

Ingår i 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.

Ingår i The European Heart Journal - Digital Health, s. 180-189, 2024

• DOI för A comparison of artificial intelligence–enhanced electrocardiography approaches for the prediction of time to mortality using electrocardiogram images
• Ladda ner fulltext (pdf) av A comparison of artificial intelligence–enhanced electrocardiography approaches for the prediction of time to mortality using electrocardiogram images

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

Horta Ribeiro, Antônio; Ribeiro, Antonio Luiz P

Ingår i European Heart Journal, s. 853-855, 2024

• DOI för 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.

Ingår i npj Digital Medicine, 2024

• DOI för Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease
• Ladda ner fulltext (pdf) av Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease

Prognostic Significance and Associations of Neural Network–Derived Electrocardiographic Features

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

Ingår i Circulation. Cardiovascular Quality and Outcomes, 2024

• DOI för Prognostic Significance and Associations of Neural Network–Derived Electrocardiographic Features
• Ladda ner fulltext (pdf) av 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.

Ingår i The Lancet Digital Health, 2024

• DOI för Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study
• Ladda ner fulltext (pdf) av 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.

Ingår i The European Heart Journal - Digital Health, s. 247-259, 2024

• DOI för Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification
• Ladda ner fulltext (pdf) av Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification

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.

Ingår i Future Cardiology, s. 209-220, 2024

• DOI för 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.

Ingår i Scientific Reports, 2024

• DOI för Evaluating regression and probabilistic methods for ECG-based electrolyte prediction
• Ladda ner fulltext (pdf) av 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.

Ingår i 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.

Ingår i The European Heart Journal - Digital Health, s. 384-392, 2023

• DOI för Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival
• Ladda ner fulltext (pdf) av Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival

Association of lifestyle with deep learning predicted electrocardiographic age

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

Ingår i Frontiers in Cardiovascular Medicine, 2023

• DOI för Association of lifestyle with deep learning predicted electrocardiographic age
• Ladda ner fulltext (pdf) av Association of lifestyle with deep learning predicted electrocardiographic age

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.

Ingår i IEEE Transactions on Biomedical Engineering, s. 2227-2236, 2023

• DOI för 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.

Ingår i Circulation. Cardiovascular Quality and Outcomes, 2023

• DOI för 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.

Ingår i PLoS Neglected Tropical Diseases, 2023

• DOI för Screening for Chagas disease from the electrocardiogram using a deep neural network
• Ladda ner fulltext (pdf) av 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.

Ingår i Circulation, s. 765-777, 2023

• DOI för 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.

Ingår i Journal of Electrocardiology, s. 193-200, 2023

• DOI för End-to-end risk prediction of atrial fibrillation from the 12-Lead ECG by deep neural networks
• Ladda ner fulltext (pdf) av 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.

Ingår i IEEE Signal Processing Letters, s. 563-567, 2023

• DOI för Invertible Kernel PCA With Random Fourier Features

Overparameterized Linear Regression Under Adversarial Attacks

Horta Ribeiro, Antônio; Schön, Thomas B.

Ingår i IEEE Transactions on Signal Processing, s. 601-614, 2023

• DOI för 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.

Ingår i medRxiv, 2022

• DOI för 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.

Ingår i Scientific Reports, 2022

• DOI för Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients
• Ladda ner fulltext (pdf) av 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.

Ingår i Nature Communications, 2022

• DOI för Automated multilabel diagnosis on electrocardiographic images and signals
• Ladda ner fulltext (pdf) av 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.

Ingår i 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.

Ingår i The European Heart Journal - Digital Health, s. 576-585, 2021

• DOI för Atrial fibrillation risk prediction from the 12-lead electrocardiogram using digital biomarkers and deep representation learning
• Ladda ner fulltext (pdf) av 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.

Ingår i Hearts, s. 449-458, 2021

• DOI för 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.

Ingår i Nature Communications, 2021

• DOI för Deep neural network-estimated electrocardiographic age as a mortality predictor
• Ladda ner fulltext (pdf) av 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

Ingår i Communications of the ACM, 2020

• DOI för 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.

Ingår i Global Heart, 2020

• DOI för 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.

Ingår i Nature Methods, s. 261-272, 2020

• DOI för 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.

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

• DOI för 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.

Ingår i 2020 Computing in Cardiology (CinC), 2020

• DOI för 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.

Ingår i Nature Communications, 2020

• DOI för Automatic diagnosis of the 12-lead ECG using a deep neural network
• Ladda ner fulltext (pdf) av Automatic diagnosis of the 12-lead ECG using a deep neural network

On the smoothness of nonlinear system identification

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

Ingår i Automatica, 2020

• DOI för 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.

Ingår i Journal of Electrocardiology, 2019

• DOI för 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.

Ingår i Journal of Electrocardiology, 2019

• DOI för 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.

Ingår i Circulation. Abstracts from American Heart Association’s., 2018

Lasso Regularization Paths for NARMAX Models via Coordinate Descent

Horta Ribeiro, Antônio; Aguirre, Luis A.

Ingår i 2018 Annual American Control Conference (ACC), s. 5268-5273, 2018

• DOI för 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.

Ingår i Neurocomputing, s. 222-231, 2018

• DOI för ”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.

Ingår i IFAC-PapersOnLine, s. 13998-14003, 2017

• DOI för 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.

Ingår i IFAC Workshop on Automatic Control in Offshore Oil and Gas Production, s. 154-158, 2015

• DOI för 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.

Ingår i 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

Ingår i ICASSP 2025, 2025

• DOI för 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

Ingår i International Conference on Artificial Intelligence and Statistics, s. 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.

Ingår i Artificial Intelligence in Medicine, s. 202-206, 2025

• DOI för 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.

Ingår i International Conference on Machine Learning (ICML), s. 15271-15293, 2024

Can Transformers Smell Like Humans?

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

Ingår i Advances in Neural Information Processing Systems, 2024

Regularization properties of adversarially-trained linear regression

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

Ingår i Advances in Neural Information Processing Systems 36 (NeurIPS 2023), s. 23658-23670, 2023

• DOI för 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.

Ingår i 2021 Computing In Cardiology (CINC), 2021

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

How convolutional neural networks deal with aliasing

Horta Ribeiro, Antônio; Schön, Thomas B.

Ingår i 2021 IEEE International Conference On Acoustics, Speech And Signal Processing (ICASSP 2021), s. 2755-2759, 2021

• DOI för 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.

Ingår i Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, s. 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.

Ingår i 2020 Computing in Cardiology, 2020

• DOI för 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.

Ingår i Proc. 58th IEEE Conference on Decision and Control, s. 3670-3676, 2019

• DOI för Deep convolutional networks in system identification