24 citations · 88 across the 12 of their papers we have counts for
12 papers
HPO: We won't get fooled again
Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu
Hyperparameter optimization (HPO) is a well-studied research field. However, the effects and interactions of the components in an HPO pipeline are not yet well investigated. Then,…
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation
Aysim Toker, Lukas Kondmann, Mark Weber +10
Earth observation is a fundamental tool for monitoring the evolution of land use in specific areas of interest. Observing and precisely defining change, in this context, requires b…
Landscape of Neural Architecture Search across sensors: how much do they differ ?
Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu
With the rapid rise of neural architecture search, the ability to understand its complexity from the perspective of a search algorithm is desirable. Recently, Traoré et al. have pr…
A Data-driven Approach to Neural Architecture Search Initialization
Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu
Algorithmic design in neural architecture search (NAS) has received a lot of attention, aiming to improve performance and reduce computational cost. Despite the great advances made…
Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems
Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu
Neural architecture search is a promising area of research dedicated to automating the design of neural network models. This field is rapidly growing, with a surge of methodologies…
Lessons from the Clustering Analysis of a Search Space: A Centroid-based Approach to Initializing NAS
Kalifou Rene Traore, Andrés Camero, Xiao Xiang Zhu
Lots of effort in neural architecture search (NAS) research has been dedicated to algorithmic development, aiming at designing more efficient and less costly methods. Nonetheless,…
Reliable and Fast Recurrent Neural Network Architecture Optimization
Andrés Camero, Jamal Toutouh, Enrique Alba
This article introduces Random Error Sampling-based Neuroevolution (RESN), a novel automatic method to optimize recurrent neural network architectures. RESN combines an evolutionar…
Bayesian Neural Architecture Search using A Training-Free Performance Metric
Andrés Camero, Hao Wang, Enrique Alba +1
Recurrent neural networks (RNNs) are a powerful approach for time series prediction. However, their performance is strongly affected by their architecture and hyperparameter settin…
JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training
José Á. Morell, Andrés Camero, Enrique Alba
In 2019, around 57\% of the population of the world has broadband access to the Internet. Moreover, there are 5.9 billion mobile broadband subscriptions, i.e., 1.3 subscriptions pe…
Random Error Sampling-based Recurrent Neural Network Architecture Optimization
Andrés Camero, Jamal Toutouh, Enrique Alba
Recurrent neural networks are good at solving prediction problems. However, finding a network that suits a problem is quite hard because their performance is strongly affected by t…
DLOPT: Deep Learning Optimization Library
Andrés Camero, Jamal Toutouh, Enrique Alba
Deep learning hyper-parameter optimization is a tough task. Finding an appropriate network configuration is a key to success, however most of the times this labor is roughly done.…
Low-Cost Recurrent Neural Network Expected Performance Evaluation
Andrés Camero, Jamal Toutouh, Enrique Alba
Recurrent neural networks are a powerful tool, but they are very sensitive to their hyper-parameter configuration. Moreover, training properly a recurrent neural network is a tough…