Instance-wise Feature Grouping
Authors: Aria Masoomi, Chieh Wu, Tingting Zhao, Zifeng Wang, Peter Castaldi, Jennifer Dy
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic data validate our theoretical claims. Experiments on MNIST, Fashion MNIST, and gene expression datasets show that our method discovers feature groups with high classification accuracies. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, US 2Channing Division of Network Medicine, Brigham and Women s Hospital, Boston, MA, US |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It describes the method textually and visually with a flowchart. |
| Open Source Code | Yes | We make the source code publicly available at https://github.com/ariahimself/Instance-wise-Feature-Grouping. |
| Open Datasets | Yes | We additionally test on benchmark image datasets from MNIST, and Fashion MNIST (F-MNIST) [34, 35]... We also evaluate our method on a real-world gene expression data as quantified by RNA sequencing from the COPDGene Study, an observational study to identify genomic markers associated with chronic obstructive pulmonary disease (COPD) [32]. |
| Dataset Splits | Yes | We generate 100000 training, 1000 validation, and 1000 test samples for each combination. ... All λs are identified by maximizing the objective given a validation set. |
| Hardware Specification | Yes | The experiments are implemented with Python, Numpy, Sklearn, and Tensor Flow [36, 37, 38, 39] on a single NVIDIA GTX 1060Ti GPU. |
| Software Dependencies | No | The paper mentions software like Python, Numpy, Sklearn, and Tensor Flow but does not specify their version numbers, which are required for a reproducible description of ancillary software. |
| Experiment Setup | Yes | We use a neural network of width 100 and depth 2 to generate the probability inputs for the Gumbel-Softmax to obtain G and S; the Gumbel temperature was set to 0.1. ReLU was used as the activation function with softmax at the final layer for prediction. Adam optimizer with a learning rate of 0.001 and hyperparameters β1 = 0.9, β2 = 0.999 was used without further tuning. All datasets are centered to 0 and normalized to have a standard deviation of 1. For all data, we used two fully connected layers of width 32 and 16. All λs are identified by maximizing the objective given a validation set. |