Paulina Grnarova

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 116 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 58 citations into it; #9,363 of 21,575 by trust here, #20,271 by citations
• Generative Models — 58 citations into it
• Artificial Intelligence — 55 citations into it; #14,485 of 35,345 by trust here, #32,362 by citations
• Optimization & Training — 24 citations into it
• Safety, Robustness & Interpretability — 14 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Statistical and Nonlinear Physics — 12 citations into it
• Signal Processing — 9 citations into it

Whom Paulina Grnarova trusts overall

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

1. Alex Krizhevsky
2. Ian J. Goodfellow
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Alec Radford
7. Christian Szegedy
8. Yann LeCun
9. Luke Metz
10. Ilya Sutskever

Who trusts Paulina Grnarova overall

1. Kwot Sin Lee
2. Florian Schäfer
3. Jie Gui
4. Catherine Olsson
5. Gauthier Gidel
6. Adnan Siraj Rakin
7. Md. Zahangir Alom
8. Rushil Anirudh
9. Adnan Qayyum
10. Ngoc-Trung Tran

Papers from Paulina Grnarova's perspective

Ordered by how strongly the researchers Paulina Grnarova 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Evaluating GANs via Duality (2018) — 8 citations
• Defending Against Adversarial Attacks by Leveraging an Entire GAN (2018) — 40 citations
• A Domain Agnostic Measure for Monitoring and Evaluating GANs (2018) — 41 citations
• An Online Learning Approach to Generative Adversarial Networks (2017) — 92 citations