Deep learning for supervised classification of temporal data in ecology
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[1] Trevor H. Booth,et al. bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies , 2014 .
[2] C. Capinha,et al. Predicting the timing of ecological phenomena using dates of species occurrence records: a methodological approach and test case with mushrooms , 2019, International journal of biometeorology.
[3] David Haussler,et al. A Discriminative Framework for Detecting Remote Protein Homologies , 2000, J. Comput. Biol..
[4] Germain Forestier,et al. Deep learning for time series classification: a review , 2018, Data Mining and Knowledge Discovery.
[5] Philip G Brodrick,et al. Uncovering Ecological Patterns with Convolutional Neural Networks. , 2019, Trends in ecology & evolution.
[6] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[7] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[8] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[9] R. Real,et al. Transferability of environmental favourability models in geographic space : The case of the Iberian desman (Galemys pyrenaicus) in Portugal and Spain , 2009 .
[10] Junliang Liu,et al. Convolutional neural networks for time series classification , 2017 .
[11] Tim Oates,et al. Time series classification from scratch with deep neural networks: A strong baseline , 2016, 2017 International Joint Conference on Neural Networks (IJCNN).
[12] Stephen Marsland,et al. Wavelet filters for automated recognition of birdsong in long‐time field recordings , 2020, Methods in Ecology and Evolution.
[13] Parisa Rashidi,et al. Sequential Interpretability: Methods, Applications, and Future Direction for Understanding Deep Learning Models in the Context of Sequential Data , 2020, ArXiv.
[14] Eamonn J. Keogh,et al. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances , 2016, Data Mining and Knowledge Discovery.
[15] Yoshua Bengio,et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.
[16] Joachim Denzler,et al. Deep learning and process understanding for data-driven Earth system science , 2019, Nature.
[17] C. J. Camphuysen,et al. Flap or soar? How a flight generalist responds to its aerial environment , 2016, Philosophical Transactions of the Royal Society B: Biological Sciences.
[18] Sovan Lek,et al. Artificial neural networks as a tool in ecological modelling, an introduction , 1999 .
[19] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[20] Konstantinos Fysarakis,et al. Insect Biometrics: Optoacoustic Signal Processing and Its Applications to Remote Monitoring of McPhail Type Traps , 2015, PloS one.
[21] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[22] Emma Izquierdo-Verdiguier,et al. Understanding deep learning in land use classification based on Sentinel-2 time series , 2020, Scientific Reports.
[23] Olaf Conrad,et al. Climatologies at high resolution for the earth’s land surface areas , 2016, Scientific Data.
[24] Julian D Olden,et al. Machine Learning Methods Without Tears: A Primer for Ecologists , 2008, The Quarterly Review of Biology.
[25] Olac Fuentes,et al. Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology , 2014 .
[26] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Masahiro Ryo,et al. Basic Principles of Temporal Dynamics. , 2019, Trends in ecology & evolution.
[28] Marco Willi,et al. Identifying animal species in camera trap images using deep learning and citizen science , 2018, Methods in Ecology and Evolution.
[29] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.
[30] D. Currie. Where Newton might have taken ecology , 2018, Global Ecology and Biogeography.
[31] Allen H. Hurlbert,et al. Spatiotemporal Variation in Avian Migration Phenology: Citizen Science Reveals Effects of Climate Change , 2012, PloS one.
[32] Eric Hervet,et al. Applications for deep learning in ecology , 2019, Methods in Ecology and Evolution.
[33] Keeheon Lee,et al. The Computational Limits of Deep Learning , 2020, ArXiv.
[34] Heiko Balzter,et al. Connecting Earth observation to high-throughput biodiversity data , 2017, Nature Ecology &Evolution.
[35] M. Friedl,et al. Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data , 2013 .
[36] Eamonn J. Keogh,et al. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration , 2002, Data Mining and Knowledge Discovery.
[37] M. Friedl,et al. Mapping global urban areas using MODIS 500-m data: new methods and datasets based on 'urban ecoregions'. , 2010 .
[38] Andreas Dengel,et al. TSViz: Demystification of Deep Learning Models for Time-Series Analysis , 2018, IEEE Access.
[39] Mark A. Girolami,et al. Bat detective—Deep learning tools for bat acoustic signal detection , 2017, bioRxiv.
[40] Stephen E. Fick,et al. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas , 2017 .
[41] Florian Huber,et al. Mcfly: Automated deep learning on time series , 2020, SoftwareX.
[42] E M Wolkovich,et al. Temporal ecology in the Anthropocene. , 2014, Ecology letters.
[43] L. Frelich,et al. How much does climate change threaten European forest tree species distributions? , 2018, Global change biology.
[44] Nicola Torelli,et al. ROSE: a Package for Binary Imbalanced Learning , 2014, R J..
[45] Patrick Mäder,et al. Machine learning for image based species identification , 2018, Methods in Ecology and Evolution.
[46] April E. Reside,et al. Weather, Not Climate, Defines Distributions of Vagile Bird Species , 2010, PloS one.
[47] Nicola Torelli,et al. Training and assessing classification rules with imbalanced data , 2012, Data Mining and Knowledge Discovery.
[48] Kaiyong Zhao,et al. AutoML: A Survey of the State-of-the-Art , 2019, Knowl. Based Syst..