Florian Knoll
Radiology, Nuclear Medicine and Imaging · 2011–2021
A researcher in the Science corpus: 8 papers; trusts 52 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Radiology, Nuclear Medicine and Imaging — 84 citations into it; #1,287 of 3,265 by trust here, #558 by citations
- Computer Vision and Pattern Recognition — 82 citations into it
- Computational Mechanics — 42 citations into it; #7,021 of 10,001 by trust here, #1,711 by citations
- Artificial Intelligence — 29 citations into it
- Atomic and Molecular Physics, and Optics — 21 citations into it
- Media Technology — 21 citations into it
- Evaluation & Benchmarks — 16 citations into it
- Biomedical Engineering — 12 citations into it
Whom Florian Knoll trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Michael Lustig
- Thomas Pock
- Antonin Chambolle
- Diederik P. Kingma
- Yann LeCun
- Martin Uecker
- David L. Donoho
- Gitta Kutyniok
- Zhou Wang
- Geoffrey E. Hinton
Who trusts Florian Knoll overall
- Kerstin Hammernik
- Anders Eklund
- Yang Song
- Xiaobo Qu
- Ajil Jalal
- Steen Moeller
- Hyungjin Chung
- Arvid Lundervold
- Paul Dufort
- Anuroop Sriram
Papers from Florian Knoll's perspective
Ordered by how strongly the researchers Florian Knoll 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
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues (2019) — 351 citations
- fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning. (2020) — 514 citations
- Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction (2021) — 295 citations
- End-to-End Variational Networks for Accelerated MRI Reconstruction (2020) — 436 citations
- Assessment of the generalization of learned image reconstruction and the potential for transfer learning (2019) — 246 citations
- fastMRI: An Open Dataset and Benchmarks for Accelerated MRI (2018) — 1,072 citations
- Learning a Variational Network for Reconstruction of Accelerated MRI Data (2017) — 1,801 citations
- Second order total generalized variation (TGV) for MRI (2011) — 630 citations