Dennis DeCoste

Computer Vision and Pattern Recognition · 2005–2014

A researcher in the Science corpus: 9 papers; trusts 258 researchers and is trusted by 68 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 88 citations into it; #3,329 of 21,575 by trust here, #4,402 by citations
• Artificial Intelligence — 68 citations into it; #29,969 of 35,345 by trust here, #9,557 by citations
• Statistical Learning Theory — 23 citations into it
• Probabilistic & Bayesian Methods — 18 citations into it
• Optimization & Training — 17 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Statistics and Probability — 13 citations into it
• Computational Mechanics — 12 citations into it

Whom Dennis DeCoste trusts overall

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

1. Thomas Hofmann
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Bernhard Schölkopf
7. Benjamin M. Marlin
8. Vladimir Naumovich Vapnik
9. Jason D. M. Rennie
10. Daniel D. Lee

Who trusts Dennis DeCoste overall

1. Sotirios P. Chatzis
2. Benjamin M. Marlin
3. Neil D. Lawrence
4. Xiaoyuan Su
5. Yu Zhang
6. Thorsten Joachims
7. Rahul Mazumder
8. Joonseok Lee
9. Gábor Takács
10. Richard S. Zemel

Papers from Dennis DeCoste's perspective

Ordered by how strongly the researchers Dennis DeCoste 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition (2014) — 483 citations
• Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations (2006) — 104 citations
• Large-scale kernel machines (2007) — 322 citations
• Compact Random Feature Maps (2013) — 88 citations
• HD-CNN: Hierarchical Deep Convolutional Neural Network for Image Classification (2014) — 65 citations
• Support Vector Machine Solvers (2007) — 351 citations
• Approximation Methods for Gaussian Process Regression (2007) — 202 citations
• Building Support Vector Machines with Reduced Classifier Complexity (2006) — 318 citations
• A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs (2005) — 363 citations