Learning the Positions in CountSketch
Authors: Yi Li, Honghao Lin, Simin Liu, Ali Vakilian, David Woodruff
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7 EXPERIMENTS: LOW-RANK APPROXIMATION, 8 EXPERIMENTS: SECOND-ORDER OPTIMIZATION. Our empirical results are provided in Table 7.1 for both Algorithm 2 and Algorithm 1, where the errors take an average over 10 trials. We plot in Figures 7.1 the mean errors on a logarithmic scale. |
| Researcher Affiliation | Academia | Yi Li Nanyang Technological University yili@ntu.edu.sg Honghao Lin, Simin Liu Carnegie Mellon University {honghaol, siminliu}@andrew.cmu.edu Ali Vakilian Toyota Technological Institute at Chicago vakilian@ttic.edu David P. Woodruff Carnegie Mellon University dwoodruf@andrew.cmu.edu |
| Pseudocode | Yes | Algorithm 1 Rank-k approximation of A using a sketch S, Algorithm 2 ALGLRA(SKETCH-LOWRANK), Algorithm 3 POSITION OPTIMIZATION: GREEDY SEARCH, Algorithm 4 Position optimization: Inner Product, Algorithm 5 LRA APPROXCHECK |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use the three datasets from (Indyk et al., 2019): (1, 2) Friends, Logo (image): frames from a short video of the TV show Friends and of a logo being painted; (3) Hyper (image): hyperspectral images from natural scenes. ... We use the Electric1 dataset of residential electric load measurements. ... https://archive.ics.uci.edu/ml/datasets/Electricity Load Diagrams20112014 |
| Dataset Splits | No | For the Electric dataset, the paper states '| (A, b)train| = 320, | (A, b)test| = 80'. Table A.1 lists 'Ntrain' and 'Ntest' for other datasets. However, no explicit details regarding a 'validation' split (e.g., percentages or sample counts) are provided for any dataset. |
| Hardware Specification | Yes | For the greedy method, we used several Nvidia Ge Force GTX 1080 Ti machines. For the maximum inner product method, the experiments are conducted on a laptop with a 1.90GHz CPU and 16GB RAM. The offline training is done separately using a single GPU. |
| Software Dependencies | No | The paper mentions using 'PyTorch (Paszke et al., 2019)' and refers to a 'package released in Agrawal et al. (2019)'. However, specific version numbers for these software components are not provided. |
| Experiment Setup | Yes | Experimental parameters (i.e., learning rate for gradient descent) can be found in Appendix G. For a given m, the dimensions of the four sketches were: S Rm n, R Rm d, S2 R5m n, R2 R5m d. Parameters of the algorithm: bs = 1, lr = 1.0, 10.0 for hyper and video respectively, num_it = 1000. ... In our experiments, we set bs = 20, iter = 1000 for all datasets. We set lr = 0.1 for the Electric dataset. |