Lior Yariv

Computer Vision and Pattern Recognition · 2020–2023

A researcher in the Science corpus: 5 papers; trusts 291 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 148 citations into it; #7,187 of 21,575 by trust here, #6,089 by citations
• Computer Graphics and Computer-Aided Design — 91 citations into it; #749 of 1,584 by trust here, #495 by citations
• Computational Mechanics — 77 citations into it; #2,496 of 10,001 by trust here, #1,433 by citations
• 3D Vision & Pose — 56 citations into it
• Generative Models — 43 citations into it
• Artificial Intelligence — 24 citations into it
• Aerospace Engineering — 18 citations into it
• Statistical and Nonlinear Physics — 13 citations into it

Whom Lior Yariv trusts overall

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

1. Diederik P. Kingma
2. M. Kazhdan
3. William E. Lorensen
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Alex Krizhevsky
7. Johannes L. Schönberger
8. Kaiming He
9. Jimmy Ba
10. Yann LeCun

Who trusts Lior Yariv overall

1. Songyou Peng
2. Zehao Yu
3. Jonathan T. Barron
4. Antoine Guédon
5. Ben Mildenhall
6. Qiancheng Fu
7. Andreas Geiger
8. Matthew Tancik
9. Christian Reiser
10. Eric Chan

Papers from Lior Yariv's perspective

Ordered by how strongly the researchers Lior Yariv 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Volume Rendering of Neural Implicit Surfaces (2021) — 1,381 citations
• Implicit Geometric Regularization for Learning Shapes (2020) — 1,034 citations
• MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation (2023) — 624 citations
• BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis (2023) — 311 citations
• Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance (2020) — 997 citations