Compact Representation of Uncertainty in Clustering
Authors: Craig Greenberg, Nicholas Monath, Ari Kobren, Patrick Flaherty, Andrew McGregor, Andrew McCallum
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we demonstrate the superiority of our approach over approximate methods in analyzing real-world gene expression data used in cancer treatment. |
| Researcher Affiliation | Academia | 1College of Information and Computer Sciences, University of Massachusetts Amherst 2National Institute of Standards and Technology 3Department of Mathematics and Statistics, University of Massachusetts Amherst |
| Pseudocode | Yes | The pseudocode for this dynamic program appears in Algorithm 1. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | We use breast cancer transcriptome profiling (FPKM-UQ) data from The Cancer Genome Atlas (TCGA) |
| Dataset Splits | No | The paper mentions using 11 samples from the TCGA dataset but does not provide specific details on how these samples were split into training, validation, or test sets, nor does it mention cross-validation. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | We begin by sub-selecting the 3000 features with greatest variance across the samples. We then add an infinitesimal value prior to taking the log of the remaining features... We use correlation clustering as the energy model. Pairwise similarities are exponentiated negative euclidean distances. We subtract from each the mean pairwise similarity so that similarities are both positive and negative. |