Oyebade K. Oyedotun
Université du Luxembourg
H-index: 15
Europe-Luxembourg
Top articles of Oyebade K. Oyedotun
Title | Journal | Author(s) | Publication Date |
|---|---|---|---|
A new perspective for understanding generalization gap of deep neural networks trained with large batch sizes | Applied Intelligence | Oyebade K Oyedotun Konstantinos Papadopoulos Djamila Aouada | 2023/6 |
Eye melanoma diagnosis system using statistical texture feature extraction and soft computing techniques | Journal of Biomedical Physics & Engineering | Ebenezer Obaloluwa Olaniyi Temitope Emmanuel Komolafe Oyebade Kayode Oyedotun Tolulope Tofunmi Oyemakinde Mohamed Abdelaziz | 2023/2 |
Multi-label image classification using adaptive graph convolutional networks: from a single domain to multiple domains | arXiv preprint arXiv:2301.04494 | Indel Pal Singh Enjie Ghorbel Oyebade Oyedotun Djamila Aouada | 2023/1/11 |
Why is everyone training very deep neural network with skip connections? | IEEE Transactions on Neural Networks and Learning Systems | Oyebade K Oyedotun Kassem Al Ismaeil Djamila Aouada | 2022/1/5 |
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 | Inder Pal Singh Oyebade Oyedotun Enjie Ghorbel Djamila Aouada | 2022 |
A closer look at autoencoders for unsupervised anomaly detection | Oyebade K Oyedotun Djamila Aouada | 2022/5/23 | |
Revisiting the Training of Very Deep Neural Networks without Skip Connections | Oyebade K Oyedotun Djamila Aouada Björn Ottersten | 2021/1/10 | |
Training very deep neural networks: Rethinking the role of skip connections | Neurocomputing | Oyebade K Oyedotun Kassem Al Ismaeil Djamila Aouada | 2021/6/21 |
SPARK: spacecraft recognition leveraging knowledge of space environment | arXiv preprint arXiv:2104.05978 | Mohamed Adel Musallam Kassem Al Ismaeil Oyebade Oyedotun Marcos Damian Perez Michel Poucet | 2021/4/13 |
Deep network compression with teacher latent subspace learning and lasso | Applied Intelligence | Oyebade K Oyedotun Abd El Rahman Shabayek Djamila Aouada Björn Ottersten | 2021/2 |
Why do deep neural networks with skip connections and concatenated hidden representations work? | Oyebade K Oyedotun Djamila Aouada | 2020 | |
Structured compression of deep neural networks with debiased elastic group lasso | Oyebade Oyedotun Djamila Aouada Bjorn Ottersten | 2020 | |
Deepvi: A novel framework for learning deep view-invariant human action representations using a single rgb camera | Konstantinos Papadopoulos Enjie Ghorbel Oyebade Oyedotun Djamila Aouada Björn Ottersten | 2020/11/16 | |
Going deeper with neural networks without skip connections | Oyebade K Oyedotun Djamila Aouada Björn Ottersten | 2020/10/25 | |
Improved highway network block for training very deep neural networks | IEEE Access | Oyebade K Oyedotun Djamila Aouada Björn Ottersten | 2020/9/24 |
Analyzing and Improving Very Deep Neural Networks: From Optimization, Generalization to Compression | Oyebade Oyedotun | 2020/9/24 |
Co-Authors
