Henry Friday Nweke

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 9 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 134 citations into it; #19,146 of 21,575 by trust here, #11,286 by citations
• Artificial Intelligence — 97 citations into it; #31,150 of 35,345 by trust here, #20,377 by citations
• Radiology, Nuclear Medicine and Imaging — 20 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Probabilistic & Bayesian Methods — 12 citations into it
• Pulmonary and Respiratory Medicine — 6 citations into it

Whom Henry Friday Nweke trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Yann LeCun
4. Ruslan Salakhutdinov
5. Alex Krizhevsky
6. Ilya Sutskever
7. Alex Graves
8. J. Urgen Schmidhuber
9. Diederik P. Kingma
10. Radford M. Neal

Who trusts Henry Friday Nweke overall

1. Aminu Da’u
2. M. G. Sarwar Murshed
3. Shibo Zhang
4. Naomie Binti Salim
5. Elangovan Ramanujam

Papers from Henry Friday Nweke's perspective

Ordered by how strongly the researchers Henry Friday Nweke trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions (2019) — 358 citations
• Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges (2019) — 297 citations
• Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges (2018) — 827 citations