Davy Neven

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 740 researchers and is trusted by 184 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 133 citations into it; #2,704 of 21,575 by trust here, #5,116 by citations
• Artificial Intelligence — 43 citations into it
• Media Technology — 13 citations into it; #721 of 3,621 by trust here, #872 by citations
• Model Architectures — 13 citations into it
• Environmental Engineering — 10 citations into it; #1,949 of 5,479 by trust here, #2,025 by citations
• Evaluation & Benchmarks — 9 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Davy Neven trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yann LeCun
6. Karen Simonyan
7. Yoshua Bengio
8. Jifeng Dai
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Davy Neven overall

1. Huiyu Wang
2. Ujwal D. Bonde
3. Liang-Chieh Chen
4. Daniel Bolya
5. Yukun Zhu
6. Bowen Cheng
7. Qihang Yu
8. Wenwei Zhang
9. Maxwell D. Collins
10. Simon Vandenhende

Papers from Davy Neven's perspective

Ordered by how strongly the researchers Davy Neven trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Semantic Instance Segmentation with a Discriminative Loss Function (2017) — 479 citations
• Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth (2019) — 256 citations
• Fast Scene Understanding for Autonomous Driving (2017) — 77 citations
• Towards End-to-End Lane Detection: an Instance Segmentation Approach (2018) — 804 citations
• Classification-Driven Dynamic Image Enhancement (2017) — 71 citations