Understanding l4-based Dictionary Learning: Interpretation, Stability, and Robustness
Authors: Yuexiang Zhai, Hermish Mehta, Zhengyuan Zhou, Yi Ma
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To corroborate the theoretical analysis, we also provide extensive and compelling experimental evidence with both synthetic data and real images. and 4 SIMULATIONS AND EXPERIMENTS |
| Researcher Affiliation | Collaboration | 1Department of EECS, UC Berkeley 2Byte Dance Inc. 3Stern School of Business, NYU |
| Pseudocode | No | The paper describes the MSP algorithm using mathematical equations (e.g., equation 3) but does not present it in a structured pseudocode or algorithm block format. |
| Open Source Code | Yes | Codes are available at https://github.com/hermish/ZMZM-ICLR-2020. |
| Open Datasets | Yes | MNIST dataset (Le Cun et al., 1998) and CIFAR-10 data-set (Krizhevsky et al., 2009) |
| Dataset Splits | No | The paper mentions overall sample sizes (e.g., 'n = 50, p = 20, 000' in Figure 2 and 3) and how data matrices are constructed, but it does not specify explicit training, validation, and test dataset splits with percentages or absolute counts for its experiments. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU models, or memory specifications used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, required to replicate the experiments. |
| Experiment Setup | Yes | In this simulation, we run the MSP algorithm from equation 3, using the imperfect measurements Y of different models (YN, YO, YC). As shown in Figure 2, the normalized value of W Do 4 4 /n reaches global maximum with all types of inputs when varying the level of noise, outliers, and sparse corruptions. and We run the experiments by increasing the sample size p w.r.t. the scale of imperfect measurements η, τ, β, respectively. and We run the MSP algorithm with 100 iterations on both Y and a noisy version YN, and the learned top bases are visualized in Figure 5. |