Ibomoiye Domor Mienye

Artificial Intelligence · 2022–2024

A researcher in the Science corpus: 2 papers; trusts 29 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it; #24,837 of 35,345 by trust here, #34,213 by citations
• Computer Vision and Pattern Recognition — 22 citations into it
• Model Architectures — 10 citations into it
• Optimization & Training — 9 citations into it
• Electrical and Electronic Engineering — 7 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Ibomoiye Domor Mienye trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Robert E. Schapire
2. Yoav Freund
3. Leo Breiman
4. Jerome H. Friedman
5. Tianqi Chen
6. Yanru Zhang
7. Alexey Natekin
8. A. Taha
9. Trevor J. Hastie
10. Heung-Il Suk

Who trusts Ibomoiye Domor Mienye overall

1. Azal Ahmad Khan
2. Omkar Chaudhari
3. Rohitash Chandra

Papers from Ibomoiye Domor Mienye's perspective

Ordered by how strongly the researchers Ibomoiye Domor Mienye trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications (2024) — 596 citations
• A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects (2022) — 1,035 citations