Dmitry A. Yashunin
Information Retrieval & Search · 2016
A researcher in the Science corpus: 1 paper; trusts 69 researchers and is trusted by 32 researchers here, as inferred from the citation graph.
Subfields
- Information Retrieval & Search — 35 citations into it; #178 of 1,022 by trust here, #343 by citations
- Computer Vision and Pattern Recognition — 33 citations into it; #4,946 of 21,575 by trust here, #7,938 by citations
- Algorithms & Complexity — 24 citations into it
- Signal Processing — 16 citations into it; #989 of 7,572 by trust here, #1,504 by citations
- Artificial Intelligence — 15 citations into it
- Aerospace Engineering — 8 citations into it
- Graph Learning — 6 citations into it
- Computer Networks and Communications — 5 citations into it; #1,035 of 9,886 by trust here, #1,826 by citations
Whom Dmitry A. Yashunin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yu A. Malkov
- Leonid Boytsov
- David G. Lowe
- Bilegsaikhan Naidan
- Alexander Ponomarenko
- Yury Malkov
- Artem Babenko
- Alexandr Andoni
- Jingdong Wang
- Sunil Arya
Who trusts Dmitry A. Yashunin overall
- Patrick Lewis
- Jimmy J. Lin
- Sheng-Chieh Lin
- Xueguang Ma
- Pasquale Minervini
- Ikuya Yamada
- Michael R. Glass
- Pavlin Gregor Policar
- Jean Feydy
- Wenhan Xiong
Papers from Dmitry A. Yashunin's perspective
Ordered by how strongly the researchers Dmitry A. Yashunin trusts endorse each paper. Same corpus, a different order for every researcher.
- Gradient-based learning applied to document recognition
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Distinctive Image Features from Scale-Invariant Keypoints
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)