Aysegul Dundar

Computer Vision and Pattern Recognition · 2014–2021

A researcher in the Science corpus: 7 papers; trusts 787 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 194 citations into it; #7,687 of 21,575 by trust here, #6,146 by citations
• Artificial Intelligence — 86 citations into it; #11,835 of 35,345 by trust here, #13,163 by citations
• Generative Models — 64 citations into it
• Representation & Self-Supervised Learning — 35 citations into it
• Model Architectures — 33 citations into it; #1,453 of 2,278 by trust here, #1,726 by citations
• Media Technology — 15 citations into it
• Safety, Robustness & Interpretability — 15 citations into it; #1,002 of 2,251 by trust here, #1,945 by citations
• Evaluation & Benchmarks — 10 citations into it

Whom Aysegul Dundar trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Karen Simonyan
5. Yoshua Bengio
6. Ilya Sutskever
7. Adam Coates
8. Diederik P. Kingma
9. Kaiming He
10. Christian Szegedy

Who trusts Aysegul Dundar overall

1. Aleksandr Ermolov
2. Agnieszka Mikołajczyk
3. Sachin Mehta
4. Michael Pfeiffer
5. Lars Ruthotto
6. Naveed Akhtar
7. Waseem Rawat
8. Jochen Gast
9. Ajay Mandlekar
10. Shuxuan Guo

Papers from Aysegul Dundar's perspective

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

Their papers

• Flattened Convolutional Neural Networks for Feedforward Acceleration (2014) — 264 citations
• A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks (2014) — 299 citations
• Robust Convolutional Neural Networks under Adversarial Noise (2015) — 80 citations
• Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos (2020) — 35 citations
• Dual Contrastive Loss and Attention for GANs (2021) — 70 citations
• Context Augmentation for Convolutional Neural Networks (2017) — 5 citations
• Convolutional Clustering for Unsupervised Learning (2015) — 85 citations