Avneesh Sud

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 156 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 46 citations into it; #13,457 of 21,575 by trust here, #7,421 by citations
• Computational Mechanics — 43 citations into it; #6,017 of 10,001 by trust here, #1,711 by citations
• 3D Vision & Pose — 34 citations into it; #475 of 689 by trust here, #296 by citations
• Computer Graphics and Computer-Aided Design — 24 citations into it
• Generative Models — 16 citations into it
• Aerospace Engineering — 14 citations into it

Whom Avneesh Sud trusts overall

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

1. M. Kazhdan
2. Kaiming He
3. Geoffrey E. Hinton
4. Hao Su
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Zhiqin Chen
8. Ross B. Girshick
9. Jitendra Malik
10. Yann LeCun

Who trusts Avneesh Sud overall

1. Songyou Peng
2. Biao Zhang
3. Matthew Tancik
4. Stephen Lombardi
5. Michael Niemeyer
6. Yinbo Chen
7. Julian Chibane
8. Can Wang

Papers from Avneesh Sud's perspective

Ordered by how strongly the researchers Avneesh Sud 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Local Deep Implicit Functions for 3D Shape (2019) — 457 citations
• Local Implicit Grid Representations for 3D Scenes (2020) — 622 citations