Guy Lebanon

Artificial Intelligence · 2001–2015

A researcher in the Science corpus: 8 papers; trusts 27 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #8,181 of 35,345 by trust here, #17,776 by citations
• Information Systems — 26 citations into it
• Management Science and Operations Research — 15 citations into it
• Statistical Learning Theory — 12 citations into it
• Recommender Systems — 10 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Natural Language Processing — 7 citations into it; #4,348 of 5,688 by trust here, #5,593 by citations
• Representation & Self-Supervised Learning — 7 citations into it

Whom Guy Lebanon trusts overall

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

1. Jerome H. Friedman
2. Yoav Freund
3. Stanley F. Chen
4. Leo Breiman
5. Bo Pang
6. Robert E. Schapire
7. Ken Lang
8. Dinesh Manocha
9. E.J. Candes
10. Yehuda Koren

Who trusts Guy Lebanon overall

1. Tamir Hazan
2. Michael Collins
3. R. Meir
4. Shinto Eguchi
5. Rémi Le Priol
6. Takashi Takenouchi
7. Raquel Urtasun
8. Matthias Hein
9. Gunnar Rätsch
10. Max Welling

Papers from Guy Lebanon's perspective

Ordered by how strongly the researchers Guy Lebanon 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Boosting and Maximum Likelihood for Exponential Models (2001) — 192 citations
• Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels (2010) — 78 citations
• Learning multiple-question decision trees for cold-start recommendation (2013) — 94 citations
• Personalizing LinkedIn Feed (2015) — 41 citations
• Local Low-Rank Matrix Approximation (2013) — 216 citations
• Beyond Sentiment: The Manifold of Human Emotions (2012) — 36 citations
• Unsupervised Supervised Learning II: Margin-Based Classification without Labels (2011) — 30 citations
• Diffusion Kernels on Statistical Manifolds (2005) — 288 citations