Maksim Lapin

Computer Vision and Pattern Recognition · 2013–2015

A researcher in the Science corpus: 5 papers; trusts 388 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 56 citations into it; #6,738 of 21,575 by trust here, #10,154 by citations
• Artificial Intelligence — 55 citations into it; #9,517 of 35,345 by trust here, #13,962 by citations
• Representation & Self-Supervised Learning — 26 citations into it
• Computational Mechanics — 23 citations into it; #9,032 of 10,001 by trust here, #8,258 by citations
• Evaluation & Benchmarks — 21 citations into it
• Optimization & Training — 20 citations into it
• Statistical Learning Theory — 14 citations into it
• Numerical Analysis — 9 citations into it

Whom Maksim Lapin trusts overall

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

1. Shai Shalev-Shwartz
2. Chih-Jen Lin
3. David G. Lowe
4. Geoffrey E. Hinton
5. Radford M. Neal
6. Yoshua Bengio
7. Andrea Vedaldi
8. Alex Krizhevsky
9. Karen Simonyan
10. Yann LeCun

Who trusts Maksim Lapin overall

1. Saeid Motiian
2. Yu Zhang
3. Matthias Hein
4. Gianfranco Doretto
5. Diana Benavides Prado
6. Leonard Berrada
7. Chi Su
8. Lingxi Xie
9. Pratik Jawanpuria
10. Shan You

Papers from Maksim Lapin's perspective

Ordered by how strongly the researchers Maksim Lapin 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. ImageNet: A large-scale hierarchical image database
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning using privileged information: SV M+ and weighted SVM (2013) — 142 citations
• Scalable Multitask Representation Learning for Scene Classification (2014) — 57 citations
• Loss Functions for Top-k Error: Analysis and Insights (2015) — 110 citations
• Top-k Multiclass SVM (2015) — 97 citations
• Efficient Output Kernel Learning for Multiple Tasks (2015) — 37 citations