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A Comparative Study of Three State-of-The-Art Machine Learning Algorithms for Predicting Tree Stem Radius Measured by Dendrometer: A Case Study

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Evaluation of the Transformer Architecture for Univariate Time Series Forecasting
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Practical Recommendations for Gradient-Based Training of Deep Architectures
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Global analysis of the protection status of the world’s forests
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Within-site drivers for soil nutrient variability in plantation forests: A case study from dry sub-humid New Zealand
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A survey on long short-term memory networks for time series prediction
inferredcites →https://doi.org/10.1017/s0033583500005175openalex-extracted
Chaos in biological systems
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The biomass distribution on Earth
inferredcites →https://doi.org/10.1073/pnas.2115329119openalex-extracted
The number of tree species on Earth
inferredcites →https://doi.org/10.1098/rsif.2022.0349openalex-extracted
Reconstructing radial stem size changes of trees with machine learning
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W-Transformers: A Wavelet-based Transformer Framework for Univariate Time Series Forecasting
inferredcites →https://doi.org/10.1109/wacv.2017.58openalex-extracted
Cyclical Learning Rates for Training Neural Networks
inferredcites →https://doi.org/10.1111/2041-210x.14061openalex-extracted
Machine learning and deep learning—A review for ecologists
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Understanding relationships among multiple ecosystem services
inferredcites →https://doi.org/10.1111/nph.13995openalex-extracted
Are trees able to grow in periods of stem shrinkage?
inferredcites →https://doi.org/10.1111/nph.16582openalex-extracted
Determinants of legacy effects in pine trees – implications from an irrigation‐stop experiment
inferredcites →https://doi.org/10.1111/nph.17552openalex-extracted
Why trees grow at night
inferredcites →https://doi.org/10.1117/12.2520589openalex-extracted
Super-convergence: very fast training of neural networks using large learning rates
inferredcites →https://doi.org/10.1126/science.aaa8415openalex-extracted
Machine learning: Trends, perspectives, and prospects
inferredcites →https://doi.org/10.1126/science.abn9668openalex-extracted
Harnessing the potential of nature-based solutions for mitigating and adapting to climate change
inferredcites →https://doi.org/10.1146/annurev-ecolsys-121415-032359openalex-extracted
Forests, Climate, and Public Policy: A 500-Year Interdisciplinary Odyssey
inferredcites →https://doi.org/10.1146/annurev-environ-102017-030204openalex-extracted
The Terrestrial Carbon Sink
inferredcites →https://doi.org/10.1146/annurev.es.01.110170.001253openalex-extracted
Ecological Aspects of Endogenous Rhythmicity
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-extracted
Long Short-Term Memory
inferredcites →https://doi.org/10.1175/1520-0477(2001)082<2415:fantts>2.3.co;2openalex-extracted
FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem–Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities
inferredcites →https://doi.org/10.1609/aaai.v35i12.17325openalex-extracted
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
inferredcites →https://doi.org/10.48550/arxiv.1706.03762openalex-extracted
Attention Is All You Need
inferredcites →https://doi.org/10.48550/arxiv.2202.01381openalex-extracted
ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
inferredcites →https://doi.org/10.48550/arxiv.2204.05023openalex-extracted
Machine Learning and Deep Learning -- A review for Ecologists
inferredcites →https://doi.org/10.5194/gmd-15-5481-2022openalex-extracted
Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not
inferredcites →https://doi.org/10.5194/hess-23-1199-2019openalex-extracted
Seasonal origins of soil water used by trees
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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