Emmanuel Baltsavias
Media Technology · 2004–2018
A researcher in the Science corpus: 4 papers; trusts 24 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Media Technology — 29 citations into it; #804 of 3,621 by trust here, #1,696 by citations
- Computer Vision and Pattern Recognition — 28 citations into it; #10,948 of 21,575 by trust here, #15,955 by citations
- Artificial Intelligence — 7 citations into it
- Environmental Engineering — 6 citations into it; #1,413 of 5,479 by trust here, #3,356 by citations
- Nature and Landscape Conservation — 6 citations into it
- Analytical Chemistry — 5 citations into it
Whom Emmanuel Baltsavias trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Patrick Henry Winston
- José M. Bioucas-Dias
- Marvin Minsky
- J. Ross Quinlan
- Rick L. Lawrence
- Naoto Yokoya
- Jérôme Bolte
- Naveed Akhtar
- Miguel Simões
- Fumihito Yasuma
Who trusts Emmanuel Baltsavias overall
- Jixian Zhang
- Geoffrey E. Hinton
- Xiangguo Lin
- Renwei Dian
- Volodymyr Mnih
- Thomas Blaschke
- Naoto Yokoya
- Shutao Li
- Leyuan Fang
- Gong Cheng
Papers from Emmanuel Baltsavias's perspective
Ordered by how strongly the researchers Emmanuel Baltsavias trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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)
- ImageNet classification with deep convolutional neural networks
- Densely Connected Convolutional Networks
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network (2018) — 342 citations
- Hyperspectral Super-Resolution by Coupled Spectral Unmixing (2015) — 375 citations
- Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data (2011) — 110 citations
- Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems☆ (2004) — 323 citations