Search Results for author:
Found 12 papers, 12 papers with code
Finding Foundation Models for Time Series Classification with a PreText Task
Over the past decade, Time Series Classification (TSC) has gained an increasing attention.
ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging
Our approach uses a new form of time series average, the ShapeDTW Barycentric Average.
An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set
The measurement of progress using benchmarks evaluations is ubiquitous in computer science and machine learning.
InceptionTime: Finding AlexNet for Time Series Classification
TSC is the area of machine learning tasked with the categorization (or labelling) of time series.
Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks
The method is validated on the JIGSAWS dataset for two surgical skills evaluation tasks: classification and regression.
Automatic alignment of surgical videos using kinematic data
Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide.
Adversarial Attacks on Deep Neural Networks for Time Series Classification
Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety.
Deep Neural Network Ensembles for Time Series Classification
Deep neural networks have revolutionized many fields such as computer vision and natural language processing.
Transfer learning for time series classification
Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network's weights) to a second network to be trained on a target dataset.
Deep learning for time series classification: a review
We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC.
Data augmentation using synthetic data for time series classification with deep residual networks
This is surprising as the accuracy of deep learning models for TSC could potentially be improved, especially for small datasets that exhibit overfitting, when a data augmentation method is adopted.
Evaluating surgical skills from kinematic data using convolutional neural networks
The need for automatic surgical skills assessment is increasing, especially because manual feedback from senior surgeons observing junior surgeons is prone to subjectivity and time consuming.
Ranked #1 on
Surgical Skills Evaluation
on JIGSAWS
