Jens Petersen
Computer Vision and Pattern Recognition · 2013–2023
A researcher in the Science corpus: 7 papers; trusts 101 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 106 citations into it; #14,302 of 21,575 by trust here, #5,987 by citations
- Artificial Intelligence — 86 citations into it
- Model Architectures — 36 citations into it
- Optimization & Training — 18 citations into it
- Evaluation & Benchmarks — 16 citations into it
- Ocean Engineering — 7 citations into it
Whom Jens Petersen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Sergey Ioffe
- Christian Szegedy
- Geoffrey E. Hinton
- Alex Krizhevsky
- X. Zhang
- Yoshua Bengio
- Karen Simonyan
- Diederik P. Kingma
- Ross B. Girshick
Who trusts Jens Petersen overall
Papers from Jens Petersen's perspective
Ordered by how strongly the researchers Jens Petersen trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Long Short-Term Memory
- Going deeper with convolutions
Their papers
- nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation (2020) — 9,406 citations
- nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation (2018) — 1,100 citations
- MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation (2023) — 419 citations
- Metrics Reloaded: Recommendations for image analysis validation (2022) — 479 citations
- Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. (2019) — 375 citations
- Automated Design of Deep Learning Methods for Biomedical Image Segmentation (2019) — 325 citations
- Scalable kernels for graphs with continuous attributes (2013) — 191 citations