Mihalis A. Nicolaou

Computer Vision and Pattern Recognition · 2011–2018

A researcher in the Science corpus: 6 papers; trusts 287 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 85 citations into it; #8,191 of 21,575 by trust here, #7,421 by citations
• Experimental and Cognitive Psychology — 59 citations into it; #1,923 of 9,731 by trust here, #1,540 by citations
• Artificial Intelligence — 48 citations into it; #25,541 of 35,345 by trust here, #14,438 by citations
• Signal Processing — 45 citations into it; #6,940 of 7,572 by trust here, #2,525 by citations
• Model Architectures — 22 citations into it
• Cognitive Neuroscience — 16 citations into it
• Natural Language Processing — 13 citations into it
• Optimization & Training — 11 citations into it

Whom Mihalis A. Nicolaou trusts overall

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

1. Geoffrey E. Hinton
2. Alex Graves
3. J. Urgen Schmidhuber
4. Maja Pantic
5. Diederik P. Kingma
6. Klaus R. Scherer
7. Sepp Hochreiter
8. Yoshua Bengio
9. Alex Krizhevsky
10. Kaiming He

Who trusts Mihalis A. Nicolaou overall

1. Stamos Katsigiannis
2. Dimitrios Kollias
3. Ali Mollahosseini
4. Michalis Vrigkas
5. Mohammad Soleymani
6. Jiyoung Lee
7. Naeem Ramzan
8. Xuanyi Dong
9. Behzad Hasani
10. Christophoros Nikou

Papers from Mihalis A. Nicolaou's perspective

Ordered by how strongly the researchers Mihalis A. Nicolaou 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment (2016) — 362 citations
• Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network (2016) — 895 citations
• End-to-End Multimodal Emotion Recognition Using Deep Neural Networks (2017) — 648 citations
• Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space (2011) — 505 citations
• Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond (2018) — 518 citations
• Aff-Wild: Valence and Arousal ‘In-the-Wild’ Challenge (2017) — 408 citations