Ethem Alpaydin

Artificial Intelligence · 2004–2018

A researcher in the Science corpus: 8 papers; trusts 48 researchers and is trusted by 90 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it; #3,640 of 35,345 by trust here, #8,007 by citations
• Computer Vision and Pattern Recognition — 23 citations into it; #3,591 of 21,575 by trust here, #5,754 by citations
• Representation & Self-Supervised Learning — 16 citations into it; #2,384 of 3,145 by trust here, #3,127 by citations
• Statistical Learning Theory — 12 citations into it; #219 of 1,038 by trust here, #326 by citations
• Statistics and Probability — 12 citations into it
• Model Architectures — 11 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Molecular Biology — 9 citations into it

Whom Ethem Alpaydin trusts overall

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

1. Michael I. Jordan
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Francis R. Bach
5. Bernhard Schölkopf
6. Gert R. G. Lanckriet
7. Vladimir Naumovich Vapnik
8. Jaka Demsar
9. Yann LeCun
10. Alex Krizhevsky

Who trusts Ethem Alpaydin overall

1. Hoang Nguyen
2. Alexander Pak
3. Bolin Gao
4. Cemal Okan Sakar
5. Gaeul Jeong
6. Andrew Gordon Wilson
7. Betul Erdogdu Sakar
8. David Kristjanson Duvenaud
9. Lacra Pavel
10. Patrick Paroubek

Papers from Ethem Alpaydin's perspective

Ordered by how strongly the researchers Ethem Alpaydin 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Multiple Kernel Learning Algorithms (2011) — 1,988 citations
• Localized multiple kernel learning (2008) — 313 citations
• Introduction to Machine Learning (2018) — 2,355 citations
• Unsupervised feature extraction with autoencoder trees (2017) — 59 citations
• Budding Trees (2014) — 23 citations
• Cost-conscious comparison of supervised learning algorithms over multiple data sets (2012) — 49 citations
• Soft decision trees (2012) — 150 citations
• Introduction to Machine Learning (Adaptive Computation and Machine Learning) (2004) — 375 citations