Andrei Bursuc
Computer Vision and Pattern Recognition · with Cord · 2019–2020
A researcher in the Science corpus: 5 papers; trusts 1,076 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 265 citations into it; #9,946 of 21,575 by trust here, #7,249 by citations
- Artificial Intelligence — 184 citations into it; #17,167 of 35,345 by trust here, #11,072 by citations
- Representation & Self-Supervised Learning — 165 citations into it; #2,530 of 3,145 by trust here, #1,597 by citations
- Model Architectures — 28 citations into it
- Optimization & Training — 25 citations into it
- Signal Processing — 21 citations into it
- Media Technology — 19 citations into it
- Aerospace Engineering — 17 citations into it
Whom Andrei Bursuc trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ross B. Girshick
- Yoshua Bengio
- Diederik P. Kingma
- Yann LeCun
- Karen Simonyan
- Ilya Sutskever
- David G. Lowe
Who trusts Andrei Bursuc overall
- Valerio Marsocci
- Xiao Wang
- Adrien Bardes
- Spyros Gidaris
- Simone Scardapane
- Yisheng Song
- Jaya Kalla
- Steinar Laenen
- Guo-Jun Qi
- Nikos Komodakis
Papers from Andrei Bursuc's perspective
Ordered by how strongly the researchers Andrei Bursuc trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Learning Representations by Predicting Bags of Visual Words (2020) — 130 citations
- Boosting Few-Shot Visual Learning With Self-Supervision (2019) — 453 citations
- Online Bag-of-Visual-Words Generation for Unsupervised Representation Learning (2020) — 23 citations
- Dense Classification and Implanting for Few-Shot Learning (2019) — 206 citations
- OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning (2020) — 86 citations