Alexandros Nanopoulos

Artificial Intelligence · 2001–2010

A researcher in the Science corpus: 5 papers; trusts 34 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #7,424 of 35,345 by trust here, #11,490 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #6,889 of 21,575 by trust here, #9,079 by citations
• Information Systems — 15 citations into it; #2,010 of 8,771 by trust here, #4,012 by citations
• Signal Processing — 12 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Statistical and Nonlinear Physics — 8 citations into it
• Computational Mathematics — 6 citations into it; #148 of 612 by trust here, #277 by citations
• Statistical Learning Theory — 6 citations into it

Whom Alexandros Nanopoulos trusts overall

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

1. Radford M. Neal
2. Raymond J. Mooney
3. Jerome H. Friedman
4. Geoffrey E. Hinton
5. Jon M. Kleinberg
6. Sugato Basu
7. David Ruppert
8. Wei-Yin Loh
9. Lieven De Lathauwer
10. Stéphane Lafon

Who trusts Alexandros Nanopoulos overall

1. Rohit J. Kate
2. Yi Zheng
3. Steffen Rendle
4. Yanfei Kang
5. Jessica Lin
6. Mustafa Gokce Baydogan
7. Xiaozhe Wang
8. George C. Runger
9. Yue Shi
10. Houtao Deng

Papers from Alexandros Nanopoulos's perspective

Ordered by how strongly the researchers Alexandros Nanopoulos 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. 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data (2010) — 708 citations
• MoviExplain: a recommender system with explanations (2009) — 132 citations
• Learning optimal ranking with tensor factorization for tag recommendation (2009) — 398 citations
• Tag recommendations based on tensor dimensionality reduction (2008) — 344 citations
• Feature-based classification of time-series data (2001) — 308 citations