Tolga Tasdizen

Computer Vision and Pattern Recognition · 2003–2019

A researcher in the Science corpus: 9 papers; trusts 827 researchers and is trusted by 413 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 125 citations into it; #2,076 of 21,575 by trust here, #3,645 by citations
• Artificial Intelligence — 45 citations into it; #3,012 of 35,345 by trust here, #6,103 by citations
• Model Architectures — 20 citations into it
• Representation & Self-Supervised Learning — 20 citations into it; #489 of 3,145 by trust here, #866 by citations
• Economics and Econometrics — 13 citations into it
• Biophysics — 12 citations into it
• Aerospace Engineering — 10 citations into it
• Cognitive Neuroscience — 10 citations into it

Whom Tolga Tasdizen trusts overall

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

1. Yann LeCun
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Piotr Doll'ar
5. Yoshua Bengio
6. Kaiming He
7. Jitendra Malik
8. Pablo Arbeláez
9. Antonio Torralba
10. Ross B. Girshick

Who trusts Tolga Tasdizen overall

1. Kihyuk Sohn
2. Ekin Dogus Cubuk
3. Samuli Laine
4. David Berthelot
5. Avital Oliver
6. Zizhao Zhang
7. Lu Liu
8. Geoff French
9. Lihe Yang
10. Yanbei Chen

Papers from Tolga Tasdizen's perspective

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

Their papers

• Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning (2016) — 1,270 citations
• Mutual exclusivity loss for semi-supervised deep learning (2016) — 83 citations
• Segmentation (2019) — 1,339 citations
• Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks (2013) — 92 citations
• Crowdsourcing the creation of image segmentation algorithms for connectomics (2015) — 371 citations
• Semantic Image Segmentation with Contextual Hierarchical Models (2015) — 56 citations
• A Computational Framework for Ultrastructural Mapping of Neural Circuitry (2009) — 161 citations
• Particle‐Based Simulation of Fluids (2003) — 342 citations
• Curvature-based transfer functions for direct volume rendering: methods and applications (2003) — 460 citations