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Process-guidance improves predictive performance of neural networks for carbon turnover in ecosystems
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Predictive models aren't for causal inference.
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Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks
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Using generative adversarial networks (GAN) to simulate central‐place foraging trajectories
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The evidence contained in the P-value is context dependent.
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TIML: Task-Informed Meta-Learning for Agriculture
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Deep Symbolic Regression for Recurrent Sequences
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Optimising predictive models to prioritise viral discovery in zoonotic reservoirs
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Advancing mathematics by guiding human intuition with AI
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Machine learning in landscape ecological analysis: a review of recent approaches
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Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks
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The science of the host–virus network
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Predicting the tripartite network of mosquito-borne disease
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Rewriting results sections in the language of evidence.
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Perspectives in machine learning for wildlife conservation
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Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks
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PERFICT: A Re‐imagined foundation for predictive ecology
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A roadmap towards predicting species interaction networks (across space and time)
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Relating Graph Neural Networks to Structural Causal Models
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GinJinn2: Object detection and segmentation for ecology and evolution
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The Sharpe predictor for fairness in machine learning
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Study becomes insight: Ecological learning from machine learning
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The current and future uses of machine learning in ecosystem service research.
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Highly accurate protein structure prediction with AlphaFold
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Anomaly Detection in Hyperspectral Image Using 3D-Convolutional Variational Autoencoder
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Species Distribution Modeling for Machine Learning Practitioners: A Review
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Deep learning as a tool for ecology and evolution
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TaxoNERD: deep neural models for the recognition of taxonomic entities in the ecological and evolutionary literature
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Physics-informed machine learning
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The Flora Incognita app – Interactive plant species identification
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Density estimation using deep generative neural networks
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Ensemble deep learning: A review
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Skilful precipitation nowcasting using deep generative models of radar
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Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment
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The Low-Rank Simplicity Bias in Deep Networks
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Neural Granger Causality
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Robustness to Pruning Predicts Generalization in Deep Neural Networks
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Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression
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A comparison of the value of two machine learning predictive models to support bovine tuberculosis disease control in England.
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Radical empiricism and machine learning research
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Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning
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Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology
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WILDS: A Benchmark of in-the-Wild Distribution Shifts
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Explainable artificial intelligence enhances the ecological interpretability of black‐box species distribution models
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A translucent box: interpretable machine learning in ecology
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Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth
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A graph neural network framework for causal inference in brain networks
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Transformers: State-of-the-Art Natural Language Processing
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Going further with model verification and deep learning
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sbi: A toolkit for simulation-based inference
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Pollen analysis using multispectral imaging flow cytometry and deep learning.
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Managing Fragmented Fire-Threatened Landscapes with Spatial Externalities
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When causal inference meets deep learning
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Predicting mammalian hosts in which novel coronaviruses can be generated
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Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
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Language Models are Few-Shot Learners
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Predicting into unknown space? Estimating the area of applicability of spatial prediction models
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Elucidating ecological complexity: Unsupervised learning determines global marine eco-provinces
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Employing Machine Learning for Detection of Invasive Species using Sentinel-2 and AVIRIS Data: The Case of Kudzu in the United States
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Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
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Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
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The unreasonable effectiveness of deep learning in artificial intelligence
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Universal Differential Equations for Scientific Machine Learning
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Dota 2 with Large Scale Deep Reinforcement Learning
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Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
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Neural Tangents: Fast and Easy Infinite Neural Networks in Python
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Deep double descent: where bigger models and more data hurt
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Deep learning-based methods for individual recognition in small birds
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Causality for Machine Learning
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Information in Infinite Ensembles of Infinitely-Wide Neural Networks
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A Comprehensive Survey on Transfer Learning
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Interpretable Machine Learning
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
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A deep active learning system for species identification and counting in camera trap images
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HuggingFace's Transformers: State-of-the-art Natural Language Processing
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A demonstration of unsupervised machine learning in species delimitation.
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Animal Movement Prediction Based on Predictive Recurrent Neural Network
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Neural hierarchical models of ecological populations
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Machine learning algorithms to infer trait‐matching and predict species interactions in ecological networks
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TabNet: Attentive Interpretable Tabular Learning
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A General Framework for Uncertainty Estimation in Deep Learning
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Causal Interpretations of Black-Box Models
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Development and Delivery of Species Distribution Models to Inform Decision-Making
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A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels
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The Generalization-Stability Tradeoff in Neural Network Pruning
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Implicit Regularization in Deep Matrix Factorization
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Please Stop Permuting Features: An Explanation and Alternatives
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On Exact Computation with an Infinitely Wide Neural Net
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Generalizing from a Few Examples
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One neuron versus deep learning in aftershock prediction
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Calibrating an individual-based movement model to predict functional connectivity for little owls.
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A scalable model of vegetation transitions using deep neural networks
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Fast Graph Representation Learning with PyTorch Geometric
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A comparison of deep learning and citizen science techniques for counting wildlife in aerial survey images
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AI empowers conservation biology
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Massive computational acceleration by using neural networks to emulate mechanism-based biological models
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The seven tools of causal inference, with reflections on machine learning
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Responsible AI for conservation
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Deep learning and process understanding for data-driven Earth system science
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Identifying high-priority conservation areas for avian biodiversity using species distribution modeling
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Control of Confounding and Reporting of Results in Causal Inference Studies. Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals
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Learning Not to Learn: Training Deep Neural Networks With Biased Data
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Deep neural networks are biased towards simple functions
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A convolutional neural network for detecting sea turtles in drone imagery
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A comparison of joint species distribution models for presence–absence data
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Machine learning in medicine: Addressing ethical challenges
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Fashionable Modelling with Flux
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Identifying animal species in camera trap images using deep learning and citizen science
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Automatic whale counting in satellite images with deep learning
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I can see clearly now: Reinterpreting statistical significance
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VASC: Dimension Reduction and Visualization of Single-cell RNA-seq Data by Deep Variational Autoencoder
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Machine learning for image based species identification
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Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings
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Machine Learning in Agriculture: A Review
120
Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge
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Using machine learning to advance synthesis and use of conservation and environmental evidence
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Ensemble learning: A survey
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A practical introduction to Random Forest for genetic association studies in ecology and evolution
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AI can be sexist and racist — it’s time to make it fair
125
Neural Ordinary Differential Equations
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Machine learning to classify animal species in camera trap images: applications in ecology
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Applications for deep learning in ecology
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Fast animal pose estimation using deep neural networks
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Deep learning generalizes because the parameter-function map is biased towards simple functions
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DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
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End-to-End Learning for the Deep Multivariate Probit Model
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The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
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Can species distribution models really predict the expansion of invasive species?
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Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference
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CityNet - Deep Learning Tools for Urban Ecoacoustic Assessment
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All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class Reliance
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Statistically reinforced machine learning for nonlinear patterns and variable interactions
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Environmental DNA metabarcoding: Transforming how we survey animal and plant communities
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Mastering the game of Go without human knowledge
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Forecasting biodiversity in breeding birds using best practices
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Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
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The iNaturalist Species Classification and Detection Dataset
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Bat detective—Deep learning tools for bat acoustic signal detection
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Double/Debiased Machine Learning for Treatment and Structural Parameters
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Masked Autoregressive Flow for Density Estimation
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Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
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Visualizing the effects of predictor variables in black box supervised learning models
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Ecological interactions and the Netflix problem
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Predictability of helminth parasite host range using information on geography, host traits and parasite community structure
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Equality of Opportunity in Supervised Learning
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Semantics derived automatically from language corpora contain human-like biases
153
Multidimensional biases, gaps and uncertainties in global plant occurrence information.
154
A survey of transfer learning
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Residual Networks Behave Like Ensembles of Relatively Shallow Networks
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Random forest in remote sensing: A review of applications and future directions
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XGBoost: A Scalable Tree Boosting System
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“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
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Deep Residual Learning for Image Recognition
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Emerging Technologies to Conserve Biodiversity.
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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
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Automated discovery of relationships, models and principles in ecology
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ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
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Machine learning: Trends, perspectives, and prospects
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Rodent reservoirs of future zoonotic diseases
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Stacked species distribution models and macroecological models provide congruent projections of avian species richness under climate change
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Morphological traits determine specialization and resource use in plant–hummingbird networks in the neotropics
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Imputation of missing data in life‐history trait datasets: which approach performs the best?
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Beyond species: why ecological interaction networks vary through space and time
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ImageNet classification with deep convolutional neural networks
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Geographical and taxonomic biases in research on biodiversity in human-modified landscapes
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Machine learning for computational sustainability
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Mapping where ecologists work: biases in the global distribution of terrestrial ecological observations
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Statistical inference for stochastic simulation models--theory and application.
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Support vector machines in remote sensing: A review
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Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent.
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Scikit-learn: Machine Learning in Python
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Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods
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Variable selection using random forests
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Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making
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Comparison of PubMed and Google Scholar Literature Searches
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Regularization Paths for Generalized Linear Models via Coordinate Descent.
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Species Distribution Models: Ecological Explanation and Prediction Across Space and Time
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A working guide to boosted regression trees.
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Geographical and taxonomic biases in invasion ecology.
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Text Mining Infrastructure in R
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Modelling ecological niches with support vector machines
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Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology
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Why environmental scientists are becoming Bayesians
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Classification of hyperspectral remote sensing images with support vector machines
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Quantifying Biases in Causal Models: Classical Confounding vs Collider-Stratification Bias
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Greedy function approximation: A gradient boosting machine.
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Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
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Ridge Regression: Biased Estimation for Nonorthogonal Problems
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Support vector machines for hyperspectral remote sensing classification
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A decision-theoretic generalization of on-line learning and an application to boosting
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Land Cover Classification by an Artificial Neural Network with Ancillary Information
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A training algorithm for optimal margin classifiers
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The Strength of Weak Learnability
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Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
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Resource Competition between Plankton Algae: An Experimental and Theoretical Approach
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Cross‐Validatory Choice and Assessment of Statistical Predictions
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The perceptron: a probabilistic model for information storage and organization in the brain.
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Equation of State Calculations by Fast Computing Machines
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Maximilian Pi/Pichler-and-Hartig-2022: Publication
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Xgboost: Extreme Gradient Boosting
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Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data
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europepmc: R Interface to the Europe PubMed Central RESTful Web Service
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Generalized Physics-Informed Learning through Language-Wide Differentiable Programming
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Language Models are Unsupervised Multitask Learners
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Deep Learning for Wildlife Conservation and Restoration Efforts
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Fast and robust animal pose estimation
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Alan-Turing-Institute/MLJ
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Reconciling modern machine learning practice and the bias-variance trade-off
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Currently there is no R package for GNNs available. However, we can use the 'reticulate' package (Ushey et al., 2019) to use the python packages 'torch' and 'torch_geometric' (Fey & Lenssen
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E1071: Misc Functions of the Department of Statistics
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Reticulate: Interface to “Python.
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Machine Learning for Ecology and Sustainable Natural Resource Management
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Audio-based Bird Species Identification with Deep Convolutional Neural Networks
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Machine Learning for ‘Strategic Conservation and Planning’: Patterns, Applications, Thoughts and Urgently Needed Global Progress for Sustainability
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Recognition in Terra Incognita (arXiv:1807.04975)
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wordcloud2: Create Word Cloud by “htmlwidget.
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Recognizing Bird Species in Audio Files Using Transfer Learning
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Predicting acute contact toxicity of pesticides in honeybees (Apis mellifera) through a k-nearest neighbor model.
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kknn: Weighted k-Nearest Neighbors
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Dropout: a simple way to prevent neural networks from overfitting
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Data-dependent analysis—a "garden of forking paths"— explains why many statistically significant comparisons don't hold up.
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The statistical crisis in science data-dependent analysis— A “garden of forking paths”— Explains why
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Collinearity: a review of methods to deal with it and a simulation study evaluating their performance
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MNIST handwritten digit database
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Bayesian theory (Vol. 405 )
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Bootstrap Methods: Another Look at the Jackknife
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We used the R packages 'tm
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Ensemble Methods in Machine Learning
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The strength of weak learnability
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27th Aipr Workshop: Advances in Computer-Assisted Recognition
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Gradient-based learning applied to document recognition
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Regression Shrinkage and Selection via the Lasso
244
A logical calculus of the ideas immanent in nervous activity
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Modeling Of Algal Blooms In FreshwatersUsing Artificial Neural Networks
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Pattern recognition using generalized portrait method
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Biological pattern recognition by neural networks
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This Paper Is Included in the Proceedings of the 12th Usenix Symposium on Operating Systems Design and Implementation (osdi '16). Tensorflow: a System for Large-scale Machine Learning Tensorflow: a System for Large-scale Machine Learning
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© Institute of Mathematical Statistics, 2010 To Explain or to Predict?
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Algorithms A quick guide with code chunks in R, Python, and Julia for all common ML algorithms (elasticnet
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= model(b$x, b$edge_index, b$batch) counter <<-counter + 1
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| 1011 Methods in Ecology and Evolu(cid:13)on and parasite community structure
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The following example was mostly adapted from the 'Node Classification with Graph Neural Networks' example from the torch_geometric documentation
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epochs = 3, verbose = 1, batch_size = 125) Make predictions
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Make predictions (class probabilities): head(matrix(predict(brt, newdata = xgb
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receiving more and more attention in the last years because of their ability to process non-Euclidean data such as graphs
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# Load python packages torch and torch_geometric via the reticulate R package torch = import
