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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
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
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
Data augmentation using synthetic data for time series classification with deep residual networks
TLDR
Data augmentation in deep neural networks is the process of generating artificial data in order to reduce variance of the classifier with the goal to reduce the number of errors. Expand
Evaluating surgical skills from kinematic data using convolutional neural networks
TLDR
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. Expand
New hybrid genetic algorithms for the frequency assignment problem
TLDR
This paper presents a new hybrid genetic algorithm used to solve a frequency assignment problem. Expand
Hybrid ICA-PSO algorithm for continuous optimization
TLDR
This paper proposes a new hybrid ICA-PSO algorithm designed to solve mono-objective and multi- objective problems. Expand
Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems
TLDR
The paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms for minimizing the energy consumption in embedded systems memories. Expand
Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks
TLDR
In this paper, we designed a convolutional neural network (CNN) to classify surgical skills by extracting latent patterns in the trainees’ motions performed during robotic surgery. Expand
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