Daniel Seichter

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 2 papers; trusts 85 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 31 citations into it
• Artificial Intelligence — 14 citations into it
• Model Architectures — 12 citations into it
• Optimization & Training — 7 citations into it

Whom Daniel Seichter trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Liang-Chieh Chen
5. Christian Szegedy
6. Diederik P. Kingma
7. Rob Fergus
8. Ross B. Girshick
9. X. Zhang
10. Karen Simonyan

Who trusts Daniel Seichter overall

1. Xianping Ma
2. Shervin Minaee

Papers from Daniel Seichter's perspective

Ordered by how strongly the researchers Daniel Seichter 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
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

• Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis (2020) — 316 citations
• How to get pavement distress detection ready for deep learning? A systematic approach (2017) — 365 citations