Florian Hartig
Nature and Landscape Conservation · 2008–2022
A researcher in the Science corpus: 11 papers; trusts 99 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Nature and Landscape Conservation — 208 citations into it; #6,137 of 6,941 by trust here, #1,741 by citations
- Ecological Modeling — 165 citations into it
- Ecology — 113 citations into it
- Ecology, Evolution, Behavior and Systematics — 107 citations into it
- Artificial Intelligence — 97 citations into it; #23,621 of 35,345 by trust here, #15,866 by citations
- Statistics and Probability — 77 citations into it; #8,707 of 12,932 by trust here, #3,703 by citations
- Global and Planetary Change — 72 citations into it
- Computer Vision and Pattern Recognition — 27 citations into it
Whom Florian Hartig trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Leo Breiman
- MARK A. Beaumont
- Volker Grimm
- Alex Krizhevsky
- Robert E. Schapire
- Simon N. Wood
- Daniel McFadden
- Christopher K. Wikle
- Keith Beven
- Geoffrey E. Hinton
Who trusts Florian Hartig overall
- Joeri Hermans
- Dustin Tran
- Maximilian Pichler
- Michael U Gutmann
- Volodimir Begy
- Jan C. Thiele
- S. Lockie
- Owen Thomas
- J. Cabral
- Volker Grimm
Papers from Florian Hartig's perspective
Ordered by how strongly the researchers Florian Hartig 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
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Statistical inference for stochastic simulation models--theory and application. (2011) — 403 citations
- Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference (2018) — 342 citations
- Smart spatial incentives for market-based conservation (2008) — 96 citations
- Machine learning and deep learning—A review for ecologists (2022) — 320 citations
- Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions (2018) — 263 citations
- Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure (2017) — 2,280 citations
- Mechanistic simulation models in macroecology and biogeography: state‐of‐art and prospects (2017) — 160 citations
- Functional flower traits and their diversity drive pollinator visitation (2017) — 130 citations
- Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass (2014) — 413 citations
- Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe (2013) — 105 citations
- How to understand species’ niches and range dynamics: a demographic research agenda for biogeography (2012) — 317 citations