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Deep learning for time series classification: a review
TLDR
We study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for time series classification. Expand
Weaving executability into object-oriented meta-languages
TLDR
We explore the idea of using aspect-oriented modeling to add precise action specifications with static type checking and genericity at the meta level, and examine related issues and possible solutions. Expand
InceptionTime: Finding AlexNet for Time Series Classification
TLDR
We present InceptionTime---an ensemble of deep Convolutional Neural Network (CNN) models, inspired by the Inception-v4 architecture. Expand
Qualifying input test data for model transformations
TLDR
We propose a set of rules and a framework to assess the quality of given input models for testing a given transformation. Expand
Flowdometry: An Optical Flow and Deep Learning Based Approach to Visual Odometry
TLDR
Visual odometry is a challenging task related to simultaneous localization and mapping that aims to generate a map traveled from a visual data stream. Expand
Instant Uml
TLDR
UML is the Unified Modeling Language, produced in response to the call for a standard notation for the OO application design. Expand
On Executable Meta-Languages applied to Model Transformations
TLDR
Domain specific languages for model transformation have recently generated significant interest in the model-driven engineering community; however several different model transformation language paradigms are likely to co-exist in the near future. Expand
Adversarial Attacks on Deep Neural Networks for Time Series Classification
TLDR
We introduced the concept of adversarial attacks on deep learning models for time series classification. Expand
Transfer learning for time series classification
TLDR
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. Expand
Platform Independent Web Application Modeling
TLDR
This paper discusses platform independent web application modeling in the context of model-driven engineering. Expand
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