Literals
- aiiso:code
- fcb6756be0377e520efeb8a98eba8b24
- foaf:firstName
- SERGIO
- foaf:lastName
- MORENO ÁLVAREZ
- foaf:name
- SERGIO MORENO ÁLVAREZ
- vivo:orcidId
- 0000000218589920
- ou:orcidURL
- vivo:scopusId
- 57205102620
Typed Literals
- ou:esDoctor
- True (xsd:boolean)
- ou:personalActual
- False (xsd:boolean)
Relations
- teach:teacherOf
- ou:tienePublicacion
- A Comprehensive Survey of Imbalance Correction Techniques for Hyperspectral Data Classification
- A tool to assess the communication cost of parallel kernels on heterogeneous platforms
- AAtt-CNN: Automatic Attention-Based Convolutional Neural Networks for Hyperspectral Image Classification
- Analytical Communication Performance Models as a metric in the partitioning of data-parallel kernels on heterogeneous platforms
- Cloud Implementation of Extreme Learning Machine for Hyperspectral Image Classification
- Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection
- Deep Attention-Driven HSI Scene Classification Based on Inverted Dot-Product
- Deep mixed precision for hyperspectral image classification
- Distributed Deep Learning for Remote Sensing Data Interpretation
- Enhancing Distributed Neural Network Training Through Node-Based Communications
- Federated learning meets remote sensing
- Hashing for Retrieving Long-Tailed Distributed Remote Sensing Images
- Heterogeneous gradient computing optimization for scalable deep neural networks
- Heterogeneous model parallelism for deep neural networks
- Multiple Attention-Guided Capsule Networks for Hyperspectral Image Classification
- Optimizing Distributed Deep Learning in Heterogeneous Computing Platforms for Remote Sensing Data Classification
- Parameter-Free Attention Network for Spectral-Spatial Hyperspectral Image Classification
- Performance evaluation of model-driven partitioning algorithms for data-parallel kernels on heterogeneous platforms
- Remote Sensing Image Classification Using CNNs With Balanced Gradient for Distributed Heterogeneous Computing
- Self-Supervised Learning on Small In-Domain Datasets Can Overcome Supervised Learning in Remote Sensing
- Training deep neural networks: a static load balancing approach
- ou:perteneceAGrupoInvestigacion
Inverse Relations
- Has related: ou:autorTesis