György Fazekas

Artificial Intelligence · 2016

A researcher in the Science corpus: 2 papers; trusts 22 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 12 citations into it; #14,987 of 35,345 by trust here, #25,809 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #8,031 of 21,575 by trust here, #13,815 by citations
• Signal Processing — 11 citations into it; #2,235 of 7,572 by trust here, #3,784 by citations
• Model Architectures — 10 citations into it
• Optimization & Training — 9 citations into it
• Natural Language Processing — 6 citations into it
• Speech & Audio — 5 citations into it

Whom György Fazekas trusts overall

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

1. Sander Dieleman
2. Diederik P. Kingma
3. Yoshua Bengio
4. Yann LeCun
5. Geoffrey E. Hinton
6. Sergey Ioffe
7. Tara N. Sainath
8. Aäron van den Oord
9. Alex Krizhevsky
10. Christian Szegedy

Who trusts György Fazekas overall

1. Jongpil Lee
2. Minz Won
3. Qiuqiang Kong
4. Dmitry Bogdanov
5. Osman Semih Kayhan
6. Juhan Nam
7. David W. Romero
8. Changsong Yu
9. Keunwoo Choi
10. Andrés Ferraro

Papers from György Fazekas's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Convolutional recurrent neural networks for music classification (2016) — 536 citations
• Automatic Tagging Using Deep Convolutional Neural Networks (2016) — 351 citations