Oyebade Oyedotun
|
|
||||
| Postal Address |
Université du Luxembourg 2, Avenue de l'Université L-4365 Esch-sur-Alzette |
||||
Oyebade Oyedotun received in 2015 his MSc. degree in Electrical & Electronic Engineering with a focus on machine learning and vision applications from Near East University, Lefkosa, North Cyprus. His research interests include deep learning, neural networks, machine learning and vision, pattern recognition, swarm intelligence and cognition modelling. Oyebade joined the Signal Processing & Communications Group – SIGCOM headed by Prof. Bjorn Ottersten. He will work on machine learning and vision, under the supervision of Dr. Djamila Aouada and Prof. Björn Ottersten.
Last updated on: Monday, 06 February 2017
2022
MULTI LABEL IMAGE CLASSIFICATION USING ADAPTIVE GRAPH CONVOLUTIONAL NETWORKS (ML-AGCN); ; ;
in IEEE International Conference on Image Processing (2022)
IML-GCN: Improved Multi-Label Graph Convolutional Network for Efficient yet Precise Image Classification; ; ;
in AAAI-22 Workshop Program-Deep Learning on Graphs: Methods and Applications (2022, February)
2021
Why is Everyone Training Very Deep Neural Network with Skip Connections?; ;
in IEEE Transactions on Neural Networks and Learning Systems (2021)
Training very deep neural networks: Rethinking the role of skip connections; ;
in Neurocomputing (2021)
2020
Why do Deep Neural Networks with Skip Connections and Concatenated Hidden Representations Work?;
Poster (2020, November 18)
Structured Compression of Deep Neural Networks with Debiased Elastic Group LASSO; ;
in IEEE 2020 Winter Conference on Applications of Computer Vision (WACV 20), Aspen, Colorado, US, March 2–5, 2020 (2020, March 01)
Deep network compression with teacher latent subspace learning and LASSO; ; ;
in Applied Intelligence (2020)
GOING DEEPER WITH NEURAL NETWORKS WITHOUT SKIP CONNECTIONS; ; ;
in IEEE International Conference on Image Processing (ICIP 2020), Abu Dhabi, UAE, Oct 25–28, 2020 (2020, May 30)
DeepVI: A Novel Framework for Learning Deep View-Invariant Human Action Representations using a Single RGB Camera; ; ; ;
in IEEE International Conference on Automatic Face and Gesture Recognition, Buenos Aires 18-22 May 2020 (2020)
2019
2018
Highway Network Block with Gates Constraints for Training Very Deep Networks; ; ;
in 2018 IEEE International Conference on Computer Vision and Pattern Recognition Workshop, June 18-22, 2018 (2018, June 19)
IMPROVING THE CAPACITY OF VERY DEEP NETWORKS WITH MAXOUT UNITS; ; ;
in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (2018, February 21)
2017
Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Representations; ; ; ;
in 2017 IEEE International Conference on Computer Vision Workshop (ICCVW) (2017, August 21)
Prototype Incorporated Emotional Neural Network (PI-EmNN);
in IEEE Transactions on Neural Networks and Learning Systems (2017)
Training Very Deep Networks via Residual Learning with Stochastic Input Shortcut Connections; ; ;
in 24th International Conference on Neural Information Processing, Guangzhou, China, November 14–18, 2017 (2017, July 31)
STARR - Decision SupporT and self-mAnagement system for stRoke survivoRs Vision based Rehabilitation System; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
in European Project Space on Networks, Systems and Technologies (2017)
