Stephen Lombardi
Computer Vision and Pattern Recognition · 2012–2021
A researcher in the Science corpus: 6 papers; trusts 319 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 278 citations into it; #7,337 of 21,575 by trust here, #5,161 by citations
- Generative Models — 99 citations into it
- Computer Graphics and Computer-Aided Design — 90 citations into it
- Computational Mechanics — 62 citations into it; #9,084 of 10,001 by trust here, #3,190 by citations
- 3D Vision & Pose — 45 citations into it
- Artificial Intelligence — 34 citations into it
- Aerospace Engineering — 21 citations into it
- Media Technology — 18 citations into it
Whom Stephen Lombardi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Johannes L. Schönberger
- Alex Krizhevsky
- Kaiming He
- Yoshua Bengio
- Yann LeCun
- Sergey Ioffe
- Karen Simonyan
- Jimmy Ba
Who trusts Stephen Lombardi overall
- Tomas Simon
- Stephan J. Garbin
- Baris Gecer
- Wenqi Ren
- Keunhong Park
- Shenhan Qian
- Yi-Hua Huang
- Tao Lu
- Kai Zhang
- Codruta Orniana Ancuti
Papers from Stephen Lombardi's perspective
Ordered by how strongly the researchers Stephen Lombardi trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- State of the Art on Neural Rendering (2020) — 534 citations
- Mixture of volumetric primitives for efficient neural rendering (2021) — 370 citations
- Deep appearance models for face rendering (2018) — 325 citations
- Neural volumes (2019) — 338 citations
- Bayesian Defogging (2012) — 418 citations
- Pixel-aligned Volumetric Avatars (2021) — 52 citations