Leonid Boytsov

Information Retrieval & Search · 2012–2020

A researcher in the Science corpus: 6 papers; trusts 40 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Information Retrieval & Search — 32 citations into it; #908 of 1,022 by trust here, #793 by citations
• Signal Processing — 24 citations into it; #4,218 of 7,572 by trust here, #6,083 by citations
• Computer Vision and Pattern Recognition — 22 citations into it; #7,348 of 21,575 by trust here, #14,822 by citations
• Artificial Intelligence — 18 citations into it; #7,901 of 35,345 by trust here, #19,439 by citations
• Algorithms & Complexity — 8 citations into it; #476 of 516 by trust here, #407 by citations
• Computer Graphics and Computer-Aided Design — 8 citations into it
• Computer Networks and Communications — 7 citations into it; #2,654 of 9,886 by trust here, #7,140 by citations
• Information Systems — 5 citations into it

Whom Leonid Boytsov trusts overall

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

1. Sariel Har-Peled
2. Edgar Chávez
3. Hanan Samet
4. Piotr Indyk
5. Peter Elias
6. Gonzalo Navarro
7. Bilegsaikhan Naidan
8. Rajeev Motwani
9. Christian Beecks
10. Radim Rehurek

Who trusts Leonid Boytsov overall

1. Yury Malkov
2. Alexander Ponomarenko
3. Dmitry A. Yashunin
4. Bilegsaikhan Naidan
5. Jimmy J. Lin
6. Pasquale Minervini
7. Wen Li
8. Joel M. Mackenzie
9. Yu A. Malkov
10. Ben Harwood

Papers from Leonid Boytsov's perspective

Ordered by how strongly the researchers Leonid Boytsov trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. ImageNet Large Scale Visual Recognition Challenge
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Distinctive Image Features from Scale-Invariant Keypoints
5. ImageNet: A large-scale hierarchical image database
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Engineering Efficient and Effective Non-metric Space Library (2013) — 130 citations
• Which BM25 Do You Mean? A Large-Scale Reproducibility Study of Scoring Variants (2020) — 67 citations
• Permutation Search Methods are Efficient, Yet Faster Search is Possible (2015) — 59 citations
• Comparative Analysis of Data Structures for Approximate Nearest Neighbor Search (2014) — 26 citations
• Learning to Prune in Metric and Non-Metric Spaces (2013) — 22 citations
• Decoding billions of integers per second through vectorization (2012) — 345 citations