Björn Waske
Media Technology · 2007–2016
A researcher in the Science corpus: 7 papers; trusts 28 researchers and is trusted by 42 researchers here, as inferred from the citation graph.
Subfields
- Media Technology — 48 citations into it; #318 of 3,621 by trust here, #348 by citations
- Artificial Intelligence — 29 citations into it
- Ecology — 28 citations into it; #1,090 of 10,717 by trust here, #4,441 by citations
- Atmospheric Science — 25 citations into it; #247 of 4,804 by trust here, #644 by citations
- Computer Vision and Pattern Recognition — 24 citations into it
- Global and Planetary Change — 15 citations into it; #2,223 of 10,225 by trust here, #4,014 by citations
- Statistical Learning Theory — 12 citations into it
- Analytical Chemistry — 10 citations into it
Whom Björn Waske trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- C. Burges
- Giles M. Foody
- Mahesh Pal
- Farid Melgani
- J. Ross Quinlan
- Yuhai Wu
- Ajay Mathur
- Jon Atli Benediktsson
- Vladimir Naumovich Vapni
- Lorenzo Bruzzone
Who trusts Björn Waske overall
- Xin Huang
- Liangpei Zhang
- Jixian Zhang
- Mercedes Eugenia Paoletti
- Li Guo
- Aaron E. Maxwell
- Ernest Yeboah Boateng
- Lefei Zhang
- Grant J. Scott
- Alexandra L’Heureux
Papers from Björn Waske's perspective
Ordered by how strongly the researchers Björn Waske trusts endorse each paper. Same corpus, a different order for every researcher.
- The Nature of Statistical Learning Theory
- 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
- Going deeper with convolutions
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet: A large-scale hierarchical image database
Their papers
- A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring (2016) — 616 citations
- Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features (2015) — 429 citations
- Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data (2010) — 308 citations
- Morphological Attribute Profiles for the Analysis of Very High Resolution Images (2010) — 730 citations
- Extended profiles with morphological attribute filters for the analysis of hyperspectral data (2010) — 389 citations
- Classifier ensembles for land cover mapping using multitemporal SAR imagery (2009) — 325 citations
- Fusion of Support Vector Machines for Classification of Multisensor Data (2007) — 449 citations