Gaussian Quadrature for Kernel Features
Authors: Tri Dao, Christopher M. De Sa, Christopher Ré
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our methods on datasets in different domains, such as MNIST and TIMIT, showing that deterministic features are faster to generate and achieve accuracy comparable to the state-of-the-art kernel methods based on random Fourier features. |
| Researcher Affiliation | Academia | Tri Dao Department of Computer Science Stanford University Stanford, CA 94305 trid@stanford.edu Christopher De Sa Department of Computer Science Cornell University Ithaca, NY 14853 cdesa@cs.cornell.edu Christopher Ré Department of Computer Science Stanford University Stanford, CA 94305 chrismre@cs.stanford.edu |
| Pseudocode | No | The paper describes methods such as dense grid construction and sparse grid construction, and refers to 'Algorithm 4.1 from Holtz [12]', but it does not provide pseudocode directly within the paper. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for its methodology or a link to a code repository. |
| Open Datasets | Yes | To evaluate the performance of deterministic feature maps, we analyzed the accuracy of a sparse ANOVA kernel on the MNIST digit classification task [16] and the TIMIT speech recognition task [5]. |
| Dataset Splits | Yes | This task consists of 70, 000 examples (60, 000 in the training dataset and 10, 000 in the test dataset) of hand-written digits which need to be classified. |
| Hardware Specification | No | The paper mentions a procedure run 'on CPU' and refers to 'architectures such as application-specific integrated circuits (ASICs)', but it does not provide specific CPU models, GPU models, or detailed hardware specifications used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency names with version numbers, such as Python or PyTorch versions. |
| Experiment Setup | Yes | Each data point corresponds to a frame (10ms) of audio data, preprocessed using the standard feature space Maximum Likelihood Linear Regression (f MMLR) [4]. The input x has dimension 40. After generating kernel features z(x) from this input, we model the corresponding phonemes y by a multinomial logistic regression model. Again, we use a sparse ANOVA kernel, which is a sum of 50 sub-kernels of the form exp( γ x S y S 2), each acting on a subset S of 5 indices. These subsets are randomly chosen a priori. To reweight the quadrature features, we sample 500 data points out of 1 million. |