Thomas Plötz

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 22 citations into it; #5,389 of 21,575 by trust here, #9,563 by citations
• Artificial Intelligence — 19 citations into it; #19,404 of 35,345 by trust here, #27,551 by citations
• Biomedical Engineering — 9 citations into it
• Model Architectures — 8 citations into it
• Signal Processing — 7 citations into it

Whom Thomas Plötz trusts overall

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

1. Attila Reiss
2. Nils Y. Hammerla
3. Ludmila I. Kuncheva
4. Subhash C. Bagui
5. Andreas Bulling
6. Geoffrey E. Hinton
7. Didier Stricker
8. Josef Kittler
9. Thomas Stiefmeier
10. Ronan Collobert

Who trusts Thomas Plötz overall

1. Florenc Demrozi
2. Dalin Zhang
3. Lina Yao
4. Jindong Wang
5. Yue Zheng
6. Attila Reiss
7. Graziano Pravadelli
8. Sreenivasan Ramasamy Ramamurthy
9. Yu Guan

Papers from Thomas Plötz's perspective

Ordered by how strongly the researchers Thomas Plötz 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Dropout: a simple way to prevent neural networks from overfitting
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Ensembles of Deep LSTM Learners for Activity Recognition using Wearables (2017) — 510 citations
• Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study (2017) — 1,139 citations
• Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables (2016) — 996 citations