3.4k citations · 5.8k across the 12 of their papers we have counts for
13 papers
On the performance of deep learning for numerical optimization: an application to protein structure prediction
Hojjat Rakhshani, Lhassane Idoumghar, Soheila Ghambari +2
Deep neural networks have recently drawn considerable attention to build and evaluate artificial learning models for perceptual tasks. Here, we present a study on the performance o…
Bypassing or flying above the obstacles? A novel multi-objective UAV path planning problem
Mahmoud Golabi, Soheila Ghambari, Julien Lepagnot +3
This study proposes a novel multi-objective integer programming model for a collision-free discrete drone path planning problem. Considering the possibility of bypassing obstacles…
From feature selection to continuous optimization
Hojjat Rakhshani, Lhassane Idoumghar, Julien Lepagnot +1
Metaheuristic algorithms (MAs) have seen unprecedented growth thanks to their successful applications in fields including engineering and health sciences. In this work, we investig…
InceptionTime: Finding AlexNet for Time Series Classification
Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier +7
This paper brings deep learning at the forefront of research into Time Series Classification (TSC). TSC is the area of machine learning tasked with the categorization (or labelling…
Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber +2
Purpose: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the num…
Automatic alignment of surgical videos using kinematic data
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber +3
Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide. With the advent of Operatio…
Adversarial Attacks on Deep Neural Networks for Time Series Classification
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber +2
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. With…
Deep Neural Network Ensembles for Time Series Classification
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber +2
Deep neural networks have revolutionized many fields such as computer vision and natural language processing. Inspired by this recent success, deep learning started to show promisi…
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…
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…
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…
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…
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…