Martin Rajchl

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 10 papers; trusts 202 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 90 citations into it; #10,675 of 21,575 by trust here, #5,890 by citations
• Artificial Intelligence — 68 citations into it; #18,203 of 35,345 by trust here, #13,760 by citations
• Cognitive Neuroscience — 27 citations into it
• Model Architectures — 22 citations into it
• Graph Learning — 19 citations into it
• Biomedical Engineering — 13 citations into it
• Optimization & Training — 13 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it

Whom Martin Rajchl trusts overall

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

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

Who trusts Martin Rajchl overall

1. Wentao Zhu
2. Tal Remez
3. Neale Ratzlaff
4. G. Oren
5. Christian Henning
6. Christian Rupprecht
7. Guotai Wang
8. Meng Tang
9. Oscar Chang
10. Murat Sensoy

Papers from Martin Rajchl's perspective

Ordered by how strongly the researchers Martin Rajchl 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks (2016) — 410 citations
• Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation (2017) — 431 citations
• Implicit Weight Uncertainty in Neural Networks (2017) — 100 citations
• Distance Metric Learning Using Graph Convolutional Networks: Application to Functional Brain Networks (2017) — 216 citations
• Automatic Shadow Detection in 2D Ultrasound (2018) — 5 citations
• Metric learning with spectral graph convolutions on brain connectivity networks (2018) — 375 citations
• Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling (2018) — 77 citations
• Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation (2017) — 460 citations
• Automated cardiovascular magnetic resonance image analysis with fully convolutional networks (2017) — 706 citations
• DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images (2017) — 85 citations