Quantization Algorithms for Random Fourier Features
Authors: Xiaoyun Li, Ping Li
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | experiments confirm the effectiveness and efficiency of the proposed method. and 5. Experiments We conduct experiments with compressed RFFs on three popular learning tasks: kernel SVM (KSVM), kernel logistic regression (KLR) and kernel ridge regression (KRR). |
| Researcher Affiliation | Industry | Xiaoyun Li, Ping Li Cognitive Computing Lab Baidu Research 10900 NE 8th St Bellevue WA 98004 USA |
| Pseudocode | Yes | Algorithm 1. Lloyd-Max Algorithm (reproduced from Wu (1992)) |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the availability of its source code. |
| Open Datasets | Yes | ASU-DB (Li et al., 2016) and LIBSVM (Chang and Lin, 2011) website are cited as sources for the datasets used. |
| Dataset Splits | No | The paper states 'We randomly split each dataset into 60% for training and 40% for testing' and 'For Cover Type, the dataset is randomly divided into training and test set with equal size', but it does not specify a validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments, such as exact GPU/CPU models or processor types. |
| Software Dependencies | No | The paper states 'LIBLINEAR (Chang and Lin, 2011) is used as the solver.' but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | The parameter C in SVM is fine tuned for every compression method, b and m respectively. and train logistic regression using Stochastic Gradient Descent (SGD) with cross-entropy loss and minibatch size 500. and We train the models for at least 50 epochs until the test accuracy stabilizes. |