Multiplying Matrices Without Multiplying
Authors: Davis Blalock, John Guttag
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments using hundreds of matrices from diverse domains show that it often runs 100 faster than exact matrix products and 10 faster than current approximate methods. Our contributions can be summarized as follows: ... Experiments across hundreds of diverse matrices demonstrate that this algorithm significantly outperforms existing alternatives. It also features theoretical quality guarantees. |
| Researcher Affiliation | Collaboration | 1Mosaic ML, San Francisco, CA, USA 2MIT CSAIL, Cambridge, MA, USA. Correspondence to: Davis Blalock <davis@mosaicml.com>. |
| Pseudocode | Yes | Algorithm 1 MADDNESSHASH; Algorithm 2 Adding The Next Level to the Hashing Tree. |
| Open Source Code | Yes | All of our code and raw numerical results are publicly available at https://smarturl.it/Maddness. |
| Open Datasets | Yes | We approximated linear classifiers on the widely used CIFAR-10 and CIFAR-100 datasets (Krizhevsky et al., 2009). ... we trained kernel classifiers on the datasets from the UCR Time Series Archive (Dau et al., 2018). ... We took the first 10 images from the first 50 classes of the Caltech101 dataset (Fei-Fei et al., 2004) as a single training set |
| Dataset Splits | No | The paper specifies training and test sets for CIFAR and Caltech101 datasets, but it does not explicitly mention a distinct 'validation' dataset split or provide universal split percentages (e.g., 80/10/10) for all datasets used. |
| Hardware Specification | Yes | All experiments use a single thread on a Macbook Pro with a 2.6GHz Intel Core i7-4960HQ processor. |
| Software Dependencies | No | The paper mentions implementing algorithms in 'C++ and Python' and using 'FBGEMM' but does not provide specific version numbers for these software components or any other libraries/solvers. |
| Experiment Setup | No | The paper mentions 'we sweep a wide range of hyperparameter values' and states 'Further details regarding our experimental setup and choices (e.g., use of a single thread) can be found in Appendix E.' However, it does not provide concrete hyperparameter values or detailed training configurations in the main text. |