Learning-Based Low-Rank Approximations
Authors: Piotr Indyk, Ali Vakilian, Yang Yuan
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that, for multiple types of data sets, a learned sketch matrix can substantially reduce the approximation loss compared to a random matrix S, sometimes by one order of magnitude. |
| Researcher Affiliation | Academia | Piotr Indyk CSAIL, MIT indyk@mit.edu; Ali Vakilian University of Wisconsin Madison vakilian@wisc.edu; Yang Yuan Tsinghua University yuanyang@tsinghua.edu.cn |
| Pseudocode | Yes | Algorithm 1 Rank-k approximation of a matrix A using a sketch matrix S; Algorithm 2 Differentiable SVD implementation |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It only mentions using PyTorch for implementation but does not share its own code. |
| Open Datasets | Yes | Videos6: Logo, Friends, Eagle. We downloaded three high resolution videos from Youtube, including logo video, Friends TV show, and eagle nest cam. From each video, we collect 500 frames of size 1920 1080 3 pixels, and use 400 (100) matrices as the training (test) set. For each frame, we resize it as a 5760 1080 matrix. (Footnote 6: They can be downloaded from http://youtu.be/L5HQo FIa T4I, http://youtu.be/xm LZs Ef XEg E and http://youtu.be/ufnf_q_3Ofg); Hyper. We use matrices from HS-SOD, a dataset for hyperspectral images from natural scenes [Imamoglu et al., 2018].; Tech. We use matrices from Tech TC-300, a dataset for text categorization [Davidov et al., 2004]. |
| Dataset Splits | No | From each video, we collect 500 frames of size 1920 1080 3 pixels, and use 400 (100) matrices as the training (test) set. The paper specifies training and test sets but does not mention a separate validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. It only mentions training times without hardware specifications. |
| Software Dependencies | No | We used the autograd feature in Py Torch to numerically compute the gradient. The paper mentions PyTorch but does not provide specific version numbers for PyTorch or other software dependencies. |
| Experiment Setup | No | The paper mentions using stochastic gradient descent and optimizing non-zero entries, but lacks specific experimental setup details such as learning rates, batch sizes, number of epochs for general training, or other hyperparameters. It mentions "running for 3000 iterations" for a specific plot (Figure 5) but not as a general experimental setup detail. |