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Found 18 papers, 12 papers with code
Regularization properties of adversarially-trained linear regression
And, conversely, the minimum-norm interpolator is the solution to adversarial training with a given radius.
End-to-end Risk Prediction of Atrial Fibrillation from the 12-Lead ECG by Deep Neural Networks
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 cardiovascular diseases such as stroke and heart failure.
Deep networks for system identification: a Survey
For this reason, we provide a survey of deep learning from a system identification perspective.
ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods
We therefore investigate if regression methods can be used for accurate ECG-based prediction of electrolyte concentrations.
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG
Conclusion: We found that for traditional 12-lead ECG based diagnosis tasks DL did not yield a meaningful improvement over FE, while it improved significantly the nontraditional regression task.
Surprises in adversarially-trained linear regression
We prove that adversarial training with small disturbances gives the solution with the minimum-norm that interpolates the training data.
Automated multilabel diagnosis on electrocardiographic images and signals
The application of artificial intelligence (AI) for automated diagnosis of electrocardiograms (ECGs) can improve care in remote settings but is limited by the reliance on infrequently available signal-based data.
Overparameterized Linear Regression under Adversarial Attacks
This fact is then exploited to establish similarities between adversarial training and parameter-shrinking methods and to study how the training might affect the robustness of the estimated models.
How Convolutional Neural Networks Deal with Aliasing
The convolutional neural network (CNN) remains an essential tool in solving computer vision problems.
Beyond Occam's Razor in System Identification: Double-Descent when Modeling Dynamics
It is typically observed that the model validation performance follows a U-shaped curve as the model complexity increases.
Deep Energy-Based NARX Models
This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data.
Deep Convolutional Networks in System Identification
Recent developments within deep learning are relevant for nonlinear system identification problems.
Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness
The exploding and vanishing gradient problem has been the major conceptual principle behind most architecture and training improvements in recurrent neural networks (RNNs) during the last decade.
On the smoothness of nonlinear system identification
We shed new light on the \textit{smoothness} of optimization problems arising in prediction error parameter estimation of linear and nonlinear systems.
Automatic diagnosis of the 12-lead ECG using a deep neural network
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models.
Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network
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 residency.
Lasso Regularization Paths for NARMAX Models via Coordinate Descent
We propose a new algorithm for estimating NARMAX models with $L_1$ regularization for models represented as a linear combination of basis functions.
"Parallel Training Considered Harmful?": Comparing series-parallel and parallel feedforward network training
Neural network models for dynamic systems can be trained either in parallel or in series-parallel configurations.
