自动微分并非系统发育梯度计算的万能药
Automatic differentiation is no panacea for phylogenetic gradient computation
AI总结：
本文对比六种系统发育似然梯度实现，发现通用自动微分虽随树规模近似线性扩展，但显著慢于专用实现，建议融合系统发育库与机器学习库。
摘要：
概率模型似然关于其参数的梯度，对于现代计算统计和机器学习至关重要。借助 TensorFlow 和 PyTorch 等通用机器学习库中实现的自动微分，这些计算可用于任意模型。尽管这些库经过高度优化，但其通用性质是否会在系统发育场景中限制算法复杂度或实现速度（相较于系统发育专用代码），目前尚不清楚。本文比较了系统发育似然函数的六种梯度实现，既单独评估，也将其作为变分推断流程的一部分进行评估。我们发现，尽管自动微分在树规模上可近似线性扩展，但相比精心实现的树似然与比率变换操作梯度计算，它要慢得多。我们得出结论：将系统发育库与机器学习库相结合的混合方法，将在未来提供速度与模型灵活性的最佳组合。
英文摘要：
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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