Sara Fridovich-Keil

Computer Vision and Pattern Recognition · 2019–2023

A researcher in the Science corpus: 5 papers; trusts 88 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 101 citations into it; #8,448 of 21,575 by trust here, #2,949 by citations
• Computer Graphics and Computer-Aided Design — 57 citations into it; #1,434 of 1,584 by trust here, #306 by citations
• Computational Mechanics — 47 citations into it
• Artificial Intelligence — 46 citations into it; #26,453 of 35,345 by trust here, #6,014 by citations
• 3D Vision & Pose — 30 citations into it
• Representation & Self-Supervised Learning — 11 citations into it

Whom Sara Fridovich-Keil trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Zhou Wang
4. Diederik P. Kingma
5. Kaiming He
6. Yoshua Bengio
7. Suttisak Wizadwongsa
8. Yann LeCun
9. Alex Yu
10. Ilya Sutskever

Who trusts Sara Fridovich-Keil overall

1. Ang Cao
2. Tao Lu
3. Matthew Tancik
4. Ben Mildenhall
5. Zeyu Yang
6. Ziyi Yang
7. Jiahe Li
8. Ruizhi Shao
9. Shenhan Qian
10. Guanjun Wu

Papers from Sara Fridovich-Keil's perspective

Ordered by how strongly the researchers Sara Fridovich-Keil 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Long Short-Term Memory

Their papers

• Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains (2020) — 3,689 citations
• Plenoxels: Radiance Fields without Neural Networks (2021) — 2,260 citations
• K-Planes: Explicit Radiance Fields in Space, Time, and Appearance (2023) — 967 citations
• Neural Kernels Without Tangents (2020) — 92 citations
• A Meta-Analysis of Overfitting in Machine Learning (2019) — 198 citations