ArticleGenome biology and evolution2023
Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation.
Article in Genome biology and evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- torchtree: Flexible Phylogenetic Model Development and Inference Using PyTorch.Systematic biology · 2026Article
- Leveraging graphical model techniques to study evolution on phylogenetic networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Many-core algorithms for high-dimensional gradients on phylogenetic trees.Bioinformatics (Oxford, England) · 2024Article
- A topology-marginal composite likelihood via a generalized phylogenetic pruning algorithm.Algorithms for molecular biology : AMB · 2023Article
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Authors and funding
7 authors.
Funding
Abstract
Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning. These calculations are readily available for arbitrary models via "automatic differentiation" implemented in general-purpose machine-learning libraries such as TensorFlow and PyTorch. Although these libraries are highly optimized, it is not clear if their general-purpose nature will limit their algorithmic complexity or implementation speed for the phylogenetic case compared to phylogenetics-specific code. In this paper, we compare six gradient implementations of the phylogenetic likelihood functions, in isolation and also as part of a variational inference procedure. We find that although automatic differentiation can scale approximately linearly in tree size, it is much slower than the carefully implemented gradient calculation for tree likelihood and ratio transformation operations. We conclude that a mixed approach combining phylogenetic libraries with machine learning libraries will provide the optimal combination of speed and model flexibility moving forward.
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Registered trials
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