David B. Lindell

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 95 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 77 citations into it; #8,168 of 21,575 by trust here, #5,754 by citations
• Computer Graphics and Computer-Aided Design — 38 citations into it; #943 of 1,584 by trust here, #950 by citations
• Computational Mechanics — 36 citations into it; #5,876 of 10,001 by trust here, #5,567 by citations
• 3D Vision & Pose — 25 citations into it
• Generative Models — 21 citations into it
• Model Architectures — 10 citations into it

Whom David B. Lindell trusts overall

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

1. Kaiming He
2. Vincent Sitzmann
3. Prajit Ramachandran
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Luc Bauwens
7. Alex Krizhevsky
8. Yoshua Bengio
9. Michael Niemeyer
10. Lars M. Mescheder

Who trusts David B. Lindell overall

1. Alex Yu
2. Cheng Sun
3. Christian Reiser
4. Stephan J. Garbin
5. Jiemin Fang
6. Matthew Tancik
7. Songyou Peng
8. Zong-Yi Li
9. Min Sun
10. Marek Kowalski

Papers from David B. Lindell's perspective

Ordered by how strongly the researchers David B. Lindell trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. 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. GENERATIVE ADVERSARIAL NETS
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Implicit Neural Representations with Periodic Activation Functions (2020) — 3,729 citations
• AutoInt: Automatic Integration for Fast Neural Volume Rendering (2020) — 297 citations
• Confocal non-line-of-sight imaging based on the light-cone transform (2018) — 494 citations