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20182020
most citedDeep learning for time series classification: a review

3.4k citations · 5.8k across the 12 of their papers we have counts for

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Showing 2018Show all

5 papers · 1 filter

stat.ML2018

Automatic hyperparameter selection in Autodock

Hojjat Rakhshani, Lhassane Idoumghar, Julien Lepagnot +2

Autodock is a widely used molecular modeling tool which predicts how small molecules bind to a receptor of known 3D structure. The current version of AutoDock uses meta-heuristic a…

cs.LG2018★ 280 cited

Transfer learning for time series classification

Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber +2

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) t…

cs.LG2018★ 3.4k cited

Deep learning for time series classification: a review

Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber +2

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been pr…

cs.CV2018★ 73 cited

Data augmentation using synthetic data for time series classification with deep residual networks

Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber +2

Data augmentation in deep neural networks is the process of generating artificial data in order to reduce the variance of the classifier with the goal to reduce the number of error…

cs.CV2018★ 121 cited

Evaluating surgical skills from kinematic data using convolutional neural networks

Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber +2

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…