Christian Reiser

Computer Vision and Pattern Recognition · 2021–2023

A researcher in the Science corpus: 3 papers; trusts 137 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 105 citations into it; #8,042 of 21,575 by trust here, #6,954 by citations
• Computer Graphics and Computer-Aided Design — 85 citations into it; #497 of 1,584 by trust here, #357 by citations
• Computational Mechanics — 54 citations into it; #3,125 of 10,001 by trust here, #1,556 by citations
• 3D Vision & Pose — 20 citations into it
• Artificial Intelligence — 9 citations into it
• Generative Models — 8 citations into it
• Aerospace Engineering — 5 citations into it

Whom Christian Reiser trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Peter Hedman
5. Alex Krizhevsky
6. Zhiqin Chen
7. Kaiming He
8. Vincent Sitzmann
9. Michael Niemeyer
10. Gernot Riegler

Who trusts Christian Reiser overall

1. Zehao Yu
2. Binbin Huang
3. Anpei Chen
4. Antoine Guédon
5. Andreas Geiger
6. Matthew Tancik
7. Haithem Turki
8. Zhiwen Fan
9. Guangcong Wang
10. Oscar Michel

Papers from Christian Reiser's perspective

Ordered by how strongly the researchers Christian Reiser 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs (2021) — 995 citations
• BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis (2023) — 311 citations
• MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes (2023) — 297 citations