Danil V. Prokhorov

Computer Vision and Pattern Recognition · 1998–2019

A researcher in the Science corpus: 9 papers; trusts 472 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 100 citations into it; #9,738 of 21,575 by trust here, #9,187 by citations
• Artificial Intelligence — 49 citations into it; #28,408 of 35,345 by trust here, #22,463 by citations
• Model Architectures — 20 citations into it
• Electrical and Electronic Engineering — 18 citations into it; #4,455 of 16,539 by trust here, #6,474 by citations
• Optimization & Training — 14 citations into it
• Aerospace Engineering — 12 citations into it; #2,737 of 6,421 by trust here, #2,026 by citations
• Reinforcement Learning — 12 citations into it
• Signal Processing — 12 citations into it

Whom Danil V. Prokhorov trusts overall

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

1. N. Schraudolph
2. Thomas G. Dietterich
3. Alexander Schrijver
4. Yann LeCun
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Marc'Aurelio Ranzato
8. Lee A. Feldkamp
9. Alex Krizhevsky
10. Alex Graves

Who trusts Danil V. Prokhorov overall

1. J. Urgen Schmidhuber
2. Jing Huang
3. Daniel Maturana
4. Suya You
5. Sebastian A. Scherer
6. Laurence Aitchison
7. Dacheng Tao
8. Yu Zhang
9. Yoshua Bengio
10. Jun Li

Papers from Danil V. Prokhorov's perspective

Ordered by how strongly the researchers Danil V. Prokhorov 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking (2015) — 639 citations
• Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning (2019) — 457 citations
• An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time (2013) — 40 citations
• Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection (2019) — 1,030 citations
• Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene (2017) — 377 citations
• Tracking via Robust Multi-task Multi-view Joint Sparse Representation (2013) — 162 citations
• A Convolutional Learning System for Object Classification in 3-D Lidar Data (2010) — 56 citations
• Simple and conditioned adaptive behavior from Kalman filter trained recurrent networks (2003) — 67 citations
• Enhanced Multi-Stream Kalman Filter Training for Recurrent Networks (1998)