Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies
Authors: Takahiro Mimori, Michiaki Hamada
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments using real benchmark datasets, Geo Phy significantly outperformed other approximate Bayesian methods that considered whole topologies. |
| Researcher Affiliation | Academia | Takahiro Mimori Waseda University RIKEN AIP EMAIL Michiaki Hamada Waseda University AIST-Waseda CBBD-OIL Nippon Medical School EMAIL |
| Pseudocode | Yes | Algorithm 1 Geo Phy algorithm |
| Open Source Code | Yes | Our implementation is found at https://github.com/m1m0r1/geophy |
| Open Datasets | Yes | We compared the marginal log-likelihood (MLL) estimates for the eight real datasets (DS1-8) [8, 6, 37, 9, 17, 43, 38, 30]. |
| Dataset Splits | No | The paper does not explicitly describe train/validation/test splits for its own model training in the conventional supervised learning sense. It performs inference on the entire given datasets. |
| Hardware Specification | Yes | We measured CPU runtimes using a single thread on a 20-core Xeon processor. |
| Software Dependencies | Yes | we replicated the Mr Bayes SS runs using Mr Bayes version 3.2.7a. |
| Experiment Setup | Yes | In all of our experiments, we used Adam optimizer with an initial learning rate of 0.0001. The learning rate was then multiplied by 0.75 after every 200,000 steps. Similar to approaches taken by Zhang and Matsen IV [42], we incorporated an annealing procedure during the initial consumption of 100,000 MC samples. Specifically, we replaced the likelihood function in the lower bound with P(Y |BĪ, Ī)β and linearly increased the inverse temperature β from 0.001 to 1 throughout the iterations. |