VaiPhy: a Variational Inference Based Algorithm for Phylogeny
Authors: Hazal Koptagel, Oskar Kviman, Harald Melin, Negar Safinianaini, Jens Lagergren
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments, In Table 1, we provide the mean LL scores and standard deviations. On all datasets except DS2, our ϕ-CSMC is the superior CSMC method (highlighted in red). The wall-clock time comparison of the methods on DS1 is presented in Fig. 3. |
| Researcher Affiliation | Academia | 1School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden 2Science for Life Laboratory, Solna, Sweden |
| Pseudocode | Yes | Alg. 1 is a high-level algorithmic description of Vai Phy; In Alg. 2, we summarize the JC sampler with an algorithmic description. |
| Open Source Code | Yes | We provide our code on Git Hub: https://github.com/Lagergren-Lab/Vai Phy. |
| Open Datasets | Yes | Here we benchmark our methods, Vai Phy, and ϕ-CSMC, in terms of LL estimates on seven real-world datasets, which we refer to as DS1-DS7 ([16, 13, 35, 17, 25, 40, 29]; in Appendix E, we provide additional information about the datasets). |
| Dataset Splits | No | No specific training/validation/test dataset splits (percentages or sample counts) are mentioned in the paper's main text. |
| Hardware Specification | Yes | The experiments are performed on a high-performance computing cluster node with two Intel Xeon Gold 6130 CPUs with 16 CPU cores each. Each node in the cluster has 96 Gi B RAM. |
| Software Dependencies | No | No specific version numbers for software dependencies (e.g., Python libraries, frameworks, or specialized solvers) are provided in the paper. |
| Experiment Setup | Yes | For all methods, we use the exact parameter configurations reported in the corresponding papers or specified in their available code. Following [26], we give all CSMC methods K = 2048 particles. We run Vai Phy and ϕ-CSMC for 200 iterations and evaluate them on Eq. (16) and Eq. (17), respectively. |