Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment
Authors: Govinda Kamath, Tavor Baharav, Ilan Shomorony
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In order to validate the Adaptive Spectral Top-k algorithm, we conducted two types of experiments: (1) controlled experiments on simulated data for a crowdsourcing model with symmetric errors; (2) pairwise sequence alignment experiments on real DNA sequencing data. |
| Researcher Affiliation | Collaboration | 1Microsoft Research New England, Cambridge, MA 2Department of Electrical Engineering, Stanford University, Stanford, CA 3Department of Electrical and Computer Engineering, University of Illinois, Urbana-Champaign, IL gokamath@microsoft.com, tavorb@stanford.edu, ilans@illinois.edu |
| Pseudocode | Yes | Algorithm 1 Spectral estimation of u |
| Open Source Code | Yes | Our code is publicly available online at github.com/Tavor B/adaptive Spectral. |
| Open Datasets | Yes | Using the Pac Bio E. coli data set [38]... We also consider the NCTC4174 dataset of [39]... [38] Pacific Biosciences Inc. Pacbio e. coli dataset, 2013. URL https://github.com/ Pacific Biosciences/Dev Net/wiki/E.-coli-Bacterial-Assembly. [39] J. Parkhill et al. National collection of type cultures (NCTC)3000. URL https://www. sanger.ac.uk/resources/downloads/bacteria/nctc/. |
| Dataset Splits | No | The paper uses datasets but does not explicitly provide specific training, validation, or test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions running Algorithm 2 with 'some slight modifications' and uses 'various budgets' but does not provide specific experimental setup details such as concrete hyperparameter values or training configurations. |