Oren Z. Kraus
Computer Vision and Pattern Recognition · 2015–2017
A researcher in the Science corpus: 5 papers; trusts 153 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 80 citations into it
- Molecular Biology — 73 citations into it; #6,356 of 17,773 by trust here, #10,347 by citations
- Biophysics — 68 citations into it; #220 of 1,196 by trust here, #394 by citations
- Artificial Intelligence — 47 citations into it
- Media Technology — 27 citations into it; #1,534 of 3,621 by trust here, #1,242 by citations
- Model Architectures — 17 citations into it
- Medical Imaging — 15 citations into it
- Computational Biology & Bioinformatics — 14 citations into it
Whom Oren Z. Kraus trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Radford M. Neal
- Alex Krizhevsky
- Yann LeCun
- Brendan J. Frey
- Erik H. W. Meijering
- Yoshua Bengio
- Pedro F. Felzenszwalb
- Jaume Amores
- L. V. D. van der Maaten
Who trusts Oren Z. Kraus overall
- Andrey Kan
- Juan C. Caicedo
- Ben T Grys
- Nick Pawlowski
- Shantanu Singh
- Sebastian Lapuschkin
- Mufti Mahmud
- Heather D. Couture
- Dara S Lo
- Anne E Carpenter
Papers from Oren Z. Kraus's perspective
Ordered by how strongly the researchers Oren Z. Kraus trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer (2017) — 3,174 citations
- Classifying and segmenting microscopy images with deep multiple instance learning (2015) — 422 citations
- Data-analysis strategies for image-based cell profiling (2017) — 696 citations
- Computer vision for high content screening (2016) — 52 citations
- Machine learning and computer vision approaches for phenotypic profiling (2017) — 154 citations