Ali O. Ulusoy

Computer Vision and Pattern Recognition · 2014–2018

A researcher in the Science corpus: 7 papers; trusts 782 researchers and is trusted by 827 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 191 citations into it; #1,522 of 21,575 by trust here, #2,888 by citations
• Aerospace Engineering — 78 citations into it; #155 of 6,421 by trust here, #362 by citations
• 3D Vision & Pose — 59 citations into it; #44 of 689 by trust here, #57 by citations
• Computational Mechanics — 54 citations into it; #487 of 10,001 by trust here, #552 by citations
• Computer Graphics and Computer-Aided Design — 41 citations into it; #896 of 1,584 by trust here, #899 by citations
• Geology — 41 citations into it
• Artificial Intelligence — 16 citations into it
• Environmental Engineering — 12 citations into it; #2,683 of 5,479 by trust here, #3,093 by citations

Whom Ali O. Ulusoy trusts overall

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

1. Changchang Wu
2. Kaiming He
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Andreas Geiger
7. Shubao Liu
8. S. M. Seitz
9. Ross B. Girshick
10. Brian Curless

Who trusts Ali O. Ulusoy overall

1. Andreas Geiger
2. Yiyi Liao
3. Gernot Riegler
4. Michael J. Black
5. Michael Niemeyer
6. Angela Dai
7. Zehao Yu
8. Songyou Peng
9. Joel Janai
10. Despoina Paschalidou

Papers from Ali O. Ulusoy's perspective

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

Their papers

• OctNet: Learning Deep 3D Representations at High Resolutions (2016) — 1,618 citations
• OctNetFusion: Learning Depth Fusion from Data (2017) — 211 citations
• Towards Probabilistic Volumetric Reconstruction Using Ray Potentials (2015) — 63 citations
• RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials (2018) — 98 citations
• Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels (2017) — 48 citations
• Patches, Planes and Probabilities: A Non-Local Prior for Volumetric 3D Reconstruction (2016) — 33 citations
• Evaluation of feature-based 3-d registration of probabilistic volumetric scenes (2014) — 15 citations