Olivia Wiles

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 956 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 212 citations into it; #4,162 of 21,575 by trust here, #5,941 by citations
• Artificial Intelligence — 160 citations into it
• Safety, Robustness & Interpretability — 84 citations into it
• Optimization & Training — 40 citations into it
• Generative Models — 37 citations into it
• Signal Processing — 36 citations into it; #4,910 of 7,572 by trust here, #4,900 by citations
• Computer Graphics and Computer-Aided Design — 25 citations into it
• Representation & Self-Supervised Learning — 24 citations into it; #2,332 of 3,145 by trust here, #2,910 by citations

Whom Olivia Wiles trusts overall

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

1. Kaiming He
2. Andrew Zisserman
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Karen Simonyan
7. Yoshua Bengio
8. Ross B. Girshick
9. Yann LeCun
10. Ilya Sutskever

Who trusts Olivia Wiles overall

1. Aliaksandr Siarohin
2. Jian Zhao
3. Lele Chen
4. Ayush Kumar Tewari
5. Samuel Albanie
6. Egor Zakharov
7. Tomas Jakab
8. Momina Masood
9. Stéphane Lathuilière
10. Andrea Vedaldi

Papers from Olivia Wiles's perspective

Ordered by how strongly the researchers Olivia Wiles 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• SynSin: End-to-End View Synthesis From a Single Image (2019) — 545 citations
• X2Face: A network for controlling face generation by using images, audio, and pose codes (2018) — 461 citations
• Self-supervised learning of a facial attribute embedding from video (2018) — 141 citations
• Improving Robustness using Generated Data (2021) — 379 citations
• Fixing Data Augmentation to Improve Adversarial Robustness (2021) — 319 citations
• Data Augmentation Can Improve Robustness (2021) — 411 citations