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Found 18 papers, 14 papers with code
The impact of data set similarity and diversity on transfer learning success in time series forecasting
Models, pre-trained on a similar or diverse source data set, have become pivotal in enhancing the efficiency and accuracy of time series forecasting on target data sets by leveraging transfer learning.
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.
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks.
Surgical Data Science -- from Concepts toward Clinical Translation
We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
An automatic framework for fusing information from differently stained consecutive digital whole slide images: A case study in renal histology
Objective: This article presents an automatic image processing framework to extract quantitative high-level information describing the micro-environment of glomeruli in consecutive whole slide images (WSIs) processed with different staining modalities of patients with chronic kidney rejection after kidney transplantation.
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.
Strategies for Training Stain Invariant CNNs
By training the network on one commonly used staining modality and applying it to images that include corresponding but differently stained tissue structures, the presented unsupervised strategies demonstrate significant improvements over standard training strategies.
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
On the Inter-relationships among Drift rate, Forgetting rate, Bias/variance profile and Error
These hypotheses lead to the concept of the sweet path, a path through the 3-d space of alternative drift rates, forgetting rates and bias/variance profiles on which generalization error will be minimized, such that slow drift is coupled with low forgetting and low bias, while rapid drift is coupled with fast forgetting and low variance.
