C. Bekas

Computer Vision and Pattern Recognition · 2007–2018

A researcher in the Science corpus: 8 papers; trusts 236 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 82 citations into it
• Artificial Intelligence — 77 citations into it; #25,124 of 35,345 by trust here, #8,810 by citations
• Materials Chemistry — 35 citations into it; #2,663 of 11,513 by trust here, #5,426 by citations
• Model Architectures — 34 citations into it
• Computational Theory and Mathematics — 33 citations into it; #4,193 of 12,182 by trust here, #1,291 by citations
• Computational Chemistry & Drug Discovery — 22 citations into it
• Optimization & Training — 19 citations into it
• Evaluation & Benchmarks — 15 citations into it

Whom C. Bekas trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Yoshua Bengio
6. Yann LeCun
7. X. Zhang
8. Diederik P. Kingma
9. Alex Graves
10. Andrew Zisserman

Who trusts C. Bekas overall

1. Haim Avron
2. Michele Benzi
3. Jok Man Tang
4. Shashanka Ubaru
5. Y. Saad
6. Petros Drineas
7. Zi-Mei Yao
8. Pavlo Molchanov
9. Umberto Michieli
10. Ryan P. Adams

Papers from C. Bekas's perspective

Ordered by how strongly the researchers C. Bekas trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• BAGAN: Data Augmentation with Balancing GAN (2018) — 357 citations
• “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models (2017) — 325 citations
• An estimator for the diagonal of a matrix (2007) — 246 citations
• Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction (2018) — 981 citations
• Mixed-precision in-memory computing (2017) — 420 citations
• TAPAS: Train-less Accuracy Predictor for Architecture Search (2018) — 78 citations
• Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy (2018) — 53 citations
• Incremental Training of Deep Convolutional Neural Networks (2018) — 21 citations