Panos Achlioptas

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 264 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 112 citations into it; #5,649 of 21,575 by trust here, #5,375 by citations
• Artificial Intelligence — 65 citations into it
• Generative Models — 46 citations into it
• Computational Mechanics — 36 citations into it; #8,730 of 10,001 by trust here, #7,501 by citations
• Geology — 15 citations into it
• Aerospace Engineering — 13 citations into it
• Computer Graphics and Computer-Aided Design — 13 citations into it; #1,357 of 1,584 by trust here, #1,205 by citations
• 3D Vision & Pose — 11 citations into it

Whom Panos Achlioptas trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yann LeCun
6. Kaiming He
7. Andrew L. Maas
8. Jimmy Ba
9. Ilya Sutskever
10. Sepp Hochreiter

Who trusts Panos Achlioptas overall

1. Zhiqin Chen
2. Yan Zhang
3. Sung Woo Park
4. Michael Niemeyer
5. Yongming Rao
6. Heewoo Jun
7. Vincent Sitzmann
8. Shitong Luo
9. Jonathan Sauder
10. Saining Xie

Papers from Panos Achlioptas's perspective

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

Their papers

• Learning Representations and Generative Models for 3D Point Clouds (2017) — 1,682 citations
• Representation Learning and Adversarial Generation of 3D Point Clouds (2017) — 92 citations
• ReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes (2020) — 487 citations