Miloš Radovanović

Artificial Intelligence · 2010–2017

A researcher in the Science corpus: 2 papers; trusts 29 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 21 citations into it; #11,871 of 35,345 by trust here, #13,760 by citations
• Computer Vision and Pattern Recognition — 16 citations into it; #7,926 of 21,575 by trust here, #10,677 by citations
• Representation & Self-Supervised Learning — 12 citations into it
• Signal Processing — 7 citations into it
• Statistics and Probability — 7 citations into it
• Statistical Learning Theory — 6 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Miloš Radovanović trusts overall

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

1. Radford M. Neal
2. Jerome H. Friedman
3. Geoffrey E. Hinton
4. David Ruppert
5. Sugato Basu
6. Wei-Yin Loh
7. Stéphane Lafon
8. Aapo Hyvärinen
9. Bo Pang
10. Elizaveta Levina

Who trusts Miloš Radovanović overall

1. Ali Cheraghian
2. Georgiana Dinu
3. Jun Shu
4. Li Zhang
5. Xirong Li
6. Shafin Rahman
7. Tao A. Xiang
8. Marco Baroni
9. Dylan Campbell
10. Zongben Xu

Papers from Miloš Radovanović's perspective

Ordered by how strongly the researchers Miloš Radovanović 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. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. ImageNet: A large-scale hierarchical image database

Their papers

• Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data (2010) — 708 citations
• The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality (2017) — 62 citations