Alexandr A Kalinin

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 10 papers; trusts 297 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 287 citations into it; #21,547 of 35,345 by trust here, #6,064 by citations
• Computer Vision and Pattern Recognition — 190 citations into it; #8,923 of 21,575 by trust here, #7,176 by citations
• Biophysics — 59 citations into it
• Model Architectures — 42 citations into it
• Radiology, Nuclear Medicine and Imaging — 41 citations into it; #926 of 3,265 by trust here, #1,628 by citations
• Media Technology — 40 citations into it
• Optimization & Training — 37 citations into it
• Management Science and Operations Research — 8 citations into it

Whom Alexandr A Kalinin trusts overall

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

1. Geoffrey E. Hinton
2. Vladimir I. Iglovikov
3. Karen Simonyan
4. Kaiming He
5. Yann LeCun
6. Alex Krizhevsky
7. Yoshua Bengio
8. Diederik P. Kingma
9. Andrew Zisserman
10. Ilya Sutskever

Who trusts Alexandr A Kalinin overall

1. Alexey A. Shvets
2. Vladimir I. Iglovikov
3. Alexander Rakhlin
4. Hasib Zunair
5. Alexander V. Buslaev
6. Selim S. Seferbekov
7. A. Ben Hamza
8. Sergey I. Nikolenko
9. Arvid Lundervold
10. Artur Kuzin

Papers from Alexandr A Kalinin's perspective

Ordered by how strongly the researchers Alexandr A Kalinin 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Opportunities and obstacles for deep learning in biology and medicine (2017) — 2,030 citations
• Albumentations: fast and flexible image augmentations (2018) — 2,644 citations
• Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning (2018) — 388 citations
• Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018) — 142 citations
• Medical Image Segmentation Using Deep Neural Networks with Pre-trained Encoders (2020) — 47 citations
• Breast Tumor Cellularity Assessment using Deep Neural Networks (2019) — 24 citations
• Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis (2018) — 343 citations
• Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks (2018) — 75 citations
• Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks (2017) — 128 citations
• 3D Cell Nuclear Morphology: Microscopy Imaging Dataset And Voxel-Based Morphometry Classification Results (2017) — 23 citations