Fred A. Hamprecht
Computer Vision and Pattern Recognition · 2009–2019
A researcher in the Science corpus: 14 papers; trusts 279 researchers and is trusted by 101 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 145 citations into it; #3,217 of 21,575 by trust here, #3,404 by citations
- Artificial Intelligence — 120 citations into it; #6,872 of 35,345 by trust here, #4,602 by citations
- Model Architectures — 38 citations into it; #653 of 2,278 by trust here, #1,285 by citations
- Optimization & Training — 36 citations into it; #2,280 of 2,601 by trust here, #1,866 by citations
- Biophysics — 28 citations into it; #422 of 1,196 by trust here, #293 by citations
- Statistics and Probability — 20 citations into it
- Evaluation & Benchmarks — 17 citations into it
- Signal Processing — 17 citations into it
Whom Fred A. Hamprecht trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Kaiming He
- Yoshua Bengio
- Yann LeCun
- Sergey Ioffe
- Christian Szegedy
- Jimmy Ba
- Max Welling
Who trusts Fred A. Hamprecht overall
- Erik J. Bekkers
- Daniel E. Worrall
- David W. Romero
- Maurice Weiler
- Taco Cohen
- Carlos Arteta
- Nichita Diaconu
- Osman Semih Kayhan
- Simon Graham
- Fanny Yang
Papers from Fred A. Hamprecht's perspective
Ordered by how strongly the researchers Fred A. Hamprecht 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
- 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
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Essentially No Barriers in Neural Network Energy Landscape (2018) — 557 citations
- Learning Steerable Filters for Rotation Equivariant CNNs (2017) — 448 citations
- On the Spectral Bias of Neural Networks (2018) — 2,472 citations
- Learning to count with regression forest and structured labels (2012) — 232 citations
- A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems (2014) — 190 citations
- On Oblique Random Forests (2011) — 256 citations
- Asymmetric Cuts: Joint Image Labeling and Partitioning (2014) — 16 citations
- Ilastik: Interactive learning and segmentation toolkit (2011) — 1,233 citations
- A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data (2009) — 1,177 citations
- An Objective Comparison of Cell Tracking Algorithms (2017) — 542 citations
- ilastik: interactive machine learning for (bio)image analysis (2019) — 2,939 citations
- End-to-End Learning of Decision Trees and Forests (2019) — 66 citations
- Cut, Glue, & Cut: A Fast, Approximate Solver for Multicut Partitioning (2014) — 48 citations
- Keeping count: Leveraging temporal context to count heavily overlapping objects (2013) — 6 citations