Dávid Rozenberszki

Computer Vision and Pattern Recognition · 2022

A researcher in the Science corpus: 1 paper; trusts 202 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 31 citations into it; #19,746 of 21,575 by trust here, #18,266 by citations
• Computational Mechanics — 17 citations into it
• Aerospace Engineering — 14 citations into it
• Artificial Intelligence — 13 citations into it
• Geology — 12 citations into it; #610 of 687 by trust here, #586 by citations
• Representation & Self-Supervised Learning — 8 citations into it
• 3D Vision & Pose — 5 citations into it

Whom Dávid Rozenberszki trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Geoffrey E. Hinton
4. Tsung-Yi Lin
5. Charles Ruizhongtai Qi
6. Alex Krizhevsky
7. Yoshua Bengio
8. Diederik P. Kingma
9. Yann LeCun
10. Ilya Sutskever

Who trusts Dávid Rozenberszki overall

1. Chandan Yeshwanth
2. Muhammad Maaz
3. Songyou Peng
4. Ayca Takmaz
5. Jonas Schult
6. Xiaoyang Wu
7. Francis Engelmann
8. Hanoona Abdul Rasheed
9. Yueh-Cheng Liu
10. Elisabetta Fedele

Papers from Dávid Rozenberszki's perspective

Ordered by how strongly the researchers Dávid Rozenberszki 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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. Going deeper with convolutions

Their papers

• Language-Grounded Indoor 3D Semantic Segmentation in the Wild (2022) — 313 citations