DiSC: Differential Spectral Clustering of Features
Authors: Ram Dyuthi Sristi, Gal Mishne, Ariel Jaffe
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Di SC on a variety of datasets, including MNIST, hyperspectral imaging, simulated sc RNA-seq and task f MRI, and demonstrate that Di SC uncovers features that better differentiate between conditions compared to competing methods. |
| Researcher Affiliation | Academia | Ram Dyuthi Sristi, Gal Mishne UC San Diego La Jolla, CA, USA {rsristi, gmishne}@ucsd.edu Ariel Jaffe Hebrew University Jerusalem, Israel ariel.jaffe@mail.huji.ac.il |
| Pseudocode | Yes | Algorithm 1 summarizes the steps of the Di SC algorithm. |
| Open Source Code | Yes | Code to reproduce the results for Sections 4.1 and 4.2 is available in https://github.com/Mishne-Lab/ Di SC |
| Open Datasets | Yes | We evaluate Di SC on a variety of datasets, including MNIST, hyperspectral imaging, simulated sc RNA-seq and task f MRI. The MNIST dataset [23] consists of images of hand written digits from 0 to 9... It has 60k training samples and 10k testing samples. ... Hyperspectral imagery change detection dataset [12]... f MRI dataset consists of 515 subjects... from the Human Connectome Project [39]. |
| Dataset Splits | No | For MNIST, the paper specifies '60k training samples and 10k testing samples', indicating a train/test split. However, it does not explicitly mention a separate validation split for any of the datasets used. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments, such as specific CPU or GPU models. The checklist states: 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No] Our algorithm is not computation expensive.' |
| Software Dependencies | No | While code is provided, the paper does not explicitly list specific software dependencies with version numbers (e.g., Python version, library versions like PyTorch, NumPy, etc.) required to reproduce the experiments. |
| Experiment Setup | Yes | The choice of the all the hyperparameters (bandwidth, d A and d B) are discussed in Appendix A. |