Back
  1. Home
  2. Profile of Oyebade OYEDOTUN (Unilu)
Profil

OYEDOTUN Oyebade

Main Referenced Co-authors
AOUADA, Djamila  (19)
OTTERSTEN, Björn  (12)
SHABAYEK, Abd El Rahman  (9)
GHORBEL, Enjie  (4)
SINGH, Inder Pal  (3)
Main Referenced Keywords
Computer Vision (2); Deep Learning (2); Deep learning (2); deep learning (2); Graph Convolutional Networks (2);
Main Referenced Unit & Research Centers
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM (13)
SnT - Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg (2)
Main Referenced Disciplines
Computer science (21)

Publications (total 21)

The most downloaded
537 downloads
OYEDOTUN, O., Al Ismaeil, K., & AOUADA, D. (2021). Why is Everyone Training Very Deep Neural Network with Skip Connections? IEEE Transactions on Neural Networks and Learning Systems. doi:10.1109/TNNLS.2021.3131813 https://hdl.handle.net/10993/48927

The most cited

42 citations (Scopus®)

OYEDOTUN, O., DEMISSE, G., SHABAYEK, A. E. R., AOUADA, D., & OTTERSTEN, B. (2017). Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Representations. In 2017 IEEE International Conference on Computer Vision Workshop (ICCVW). doi:10.1109/ICCVW.2017.374 https://hdl.handle.net/10993/32087

SINGH, I. P., GHORBEL, E., OYEDOTUN, O., & AOUADA, D. (13 July 2024). Multi-label image classification using adaptive graph convolutional networks: From a single domain to multiple domains. Computer Vision and Image Understanding, 247, 104062. doi:10.1016/j.cviu.2024.104062
Peer Reviewed verified by ORBi

OYEDOTUN, O., & AOUADA, D. (22 May 2022). A CLOSER LOOK AT AUTOENCODERS FOR UNSUPERVISED ANOMALY DETECTION [Poster presentation]. 2022 IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP).

SINGH, I. P., OYEDOTUN, O., GHORBEL, E., & AOUADA, D. (2022). IML-GCN: Improved Multi-Label Graph Convolutional Network for Efficient yet Precise Image Classification. AAAI-22 Workshop Program-Deep Learning on Graphs: Methods and Applications.
Peer reviewed

SINGH, I. P., GHORBEL, E., OYEDOTUN, O., & AOUADA, D. (2022). MULTI LABEL IMAGE CLASSIFICATION USING ADAPTIVE GRAPH CONVOLUTIONAL NETWORKS (ML-AGCN). IEEE International Conference on Image Processing.
Peer reviewed

OYEDOTUN, O., Al Ismaeil, K., & AOUADA, D. (2021). Why is Everyone Training Very Deep Neural Network with Skip Connections? IEEE Transactions on Neural Networks and Learning Systems. doi:10.1109/TNNLS.2021.3131813
Peer Reviewed verified by ORBi

OYEDOTUN, O., AL ISMAEIL, K., & AOUADA, D. (2021). Training very deep neural networks: Rethinking the role of skip connections. Neurocomputing. doi:10.1016/j.neucom.2021.02.004
Peer Reviewed verified by ORBi

OYEDOTUN, O., SHABAYEK, A. E. R., AOUADA, D., & OTTERSTEN, B. (2021). Revisiting the Training of Very Deep Neural Networks without Skip Connections [Poster presentation]. IEEE 2020 International Conference on Pattern Recognition (ICPR). doi:10.1109/ICPR48806.2021.9412508

OYEDOTUN, O., & AOUADA, D. (18 November 2020). Why do Deep Neural Networks with Skip Connections and Concatenated Hidden Representations Work? [Poster presentation]. The 27th International Conference on Neural Information Processing (ICONIP2020).

OYEDOTUN, O., SHABAYEK, A. E. R., AOUADA, D., & OTTERSTEN, B. (2020). Improved Highway Network Block for Training Very Deep Neural Networks. IEEE Access. doi:10.1109/ACCESS.2020.3026423
Peer Reviewed verified by ORBi

OYEDOTUN, O. (2020). Analyzing and Improving Very Deep Neural Networks: From Optimization, Generalization to Compression [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/44390

OYEDOTUN, O., SHABAYEK, A. E. R., AOUADA, D., & OTTERSTEN, B. (2020). Deep network compression with teacher latent subspace learning and LASSO. Applied Intelligence. doi:10.1007/s10489-020-01858-2
Peer Reviewed verified by ORBi

OYEDOTUN, O., SHABAYEK, A. E. R., AOUADA, D., & OTTERSTEN, B. (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.
Peer reviewed

OYEDOTUN, O., AOUADA, D., & OTTERSTEN, B. (2020). 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.
Peer reviewed

PAPADOPOULOS, K., GHORBEL, E., OYEDOTUN, O., AOUADA, D., & OTTERSTEN, B. (2020). 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.
Peer reviewed

OYEDOTUN, O., AOUADA, D., & OTTERSTEN, B. (14 May 2019). Learning to Fuse Latent Representations for Multimodal Data [Poster presentation]. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom.

OYEDOTUN, O., SHABAYEK, A. E. R., AOUADA, D., & OTTERSTEN, B. (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.
Peer reviewed

OYEDOTUN, O., SHABAYEK, A. E. R., AOUADA, D., & OTTERSTEN, B. (2018). IMPROVING THE CAPACITY OF VERY DEEP NETWORKS WITH MAXOUT UNITS. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing.
Peer reviewed

OYEDOTUN, O., DEMISSE, G., SHABAYEK, A. E. R., AOUADA, D., & OTTERSTEN, B. (2017). Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Representations. In 2017 IEEE International Conference on Computer Vision Workshop (ICCVW). doi:10.1109/ICCVW.2017.374
Peer reviewed

OYEDOTUN, O., & Khashman, A. (2017). Prototype Incorporated Emotional Neural Network (PI-EmNN). IEEE Transactions on Neural Networks and Learning Systems. doi:10.1109/TNNLS.2017.2730179
Peer reviewed

OYEDOTUN, O., SHABAYEK, A. E. R., AOUADA, D., & OTTERSTEN, B. (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.
Peer reviewed

SHABAYEK, A. E. R., BAPTISTA, R., PAPADOPOULOS, K., Demisse, G., OYEDOTUN, O., Antunes, M., AOUADA, D., OTTERSTEN, B., Anastassova, M., Boukallel, M., Panëels, S., Randall, G., André, M., Douchet, A., Bouilland, S., & Ortiz Fernandez, L. (2017). STARR - Decision SupporT and self-mAnagement system for stRoke survivoRs Vision based Rehabilitation System. In European Project Space on Networks, Systems and Technologies (pp. 69-80). SciTePress. doi:10.5220/0007902400690080

Contact ORBilu