Earnest Paul Ijjina

Computer Vision and Pattern Recognition · 2014–2016

A researcher in the Science corpus: 3 papers; trusts 293 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 54 citations into it; #10,071 of 21,575 by trust here, #17,258 by citations
• Artificial Intelligence — 37 citations into it; #15,750 of 35,345 by trust here, #26,682 by citations
• Biomedical Engineering — 14 citations into it; #2,796 of 10,734 by trust here, #9,794 by citations
• Human-Computer Interaction — 11 citations into it
• Optimization & Training — 8 citations into it
• Model Architectures — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Earnest Paul Ijjina trusts overall

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

1. Yoshua Bengio
2. Rasmus Berg Palm
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yann LeCun
6. Ilya Sutskever
7. J. Urgen Schmidhuber
8. Kaiming He
9. Diederik P. Kingma
10. John H. Holland

Who trusts Earnest Paul Ijjina overall

1. Liliana Lo Presti
2. Michael Edwards
3. Pichao Wang
4. Marco La Cascia
5. Xianghua Xie
6. Wanqing Li
7. Félix G. Harvey
8. Philip O. Ogunbona

Papers from Earnest Paul Ijjina's perspective

Ordered by how strongly the researchers Earnest Paul Ijjina 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Classification of human actions using pose-based features and stacked auto encoder (2016) — 68 citations
• Human Action Recognition Based on MOCAP Information Using Convolution Neural Networks (2014) — 55 citations
• Human action recognition using genetic algorithms and convolutional neural networks (2016) — 190 citations