Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Scaling Neural Tangent Kernels via Sketching and Random Features
Authors: Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We benchmark our methods on various large-scale regression and classification tasks and show that a linear regressor trained on our CNTK features matches the accuracy of exact CNTK on CIFAR-10 dataset while achieving 150 speedup. |
| Researcher Affiliation | Academia | Amir Zandieh Max-Planck-Institut für Informatik EMAIL Insu Han Yale University EMAIL Haim Avron Tel Aviv University EMAIL Neta Shoham Tel Aviv University EMAIL Chaewon Kim KAIST EMAIL Jinwoo Shin KAIST EMAIL |
| Pseudocode | Yes | Algorithm 1 NTKSKETCH for fully-connected Re LU networks ... Algorithm 2 Random Features for Re LU NTK via POLYSKETCH |
| Open Source Code | Yes | Codes are available at https://github.com/insuhan/ntk-sketch-rf. |
| Open Datasets | Yes | We first benchmark our proposed NTK approximation algorithms on MNIST [25] dataset and compare against gradient-based NTK random features [5] (GRADRF) as a baseline method. Next we test our CNTKSKETCH on CIFAR-10 dataset [24]. We also demonstrate the computational efficiency of our NTKSKETCH and NTKRF using 4 largescale UCI regression datasets [17]. |
| Dataset Splits | No | We search the ridge parameter with a random subset of training set and choose the one that achieves the best validation accuracy. The paper mentions using a "random subset of training set" for validation but does not provide specific details on the size or methodology of this subset for reproducibility. |
| Hardware Specification | Yes | We run experiments on a system with an Intel E5-2630 CPU with 256 GB RAM and a single Ge Force RTX 2080 GPUs with 12 GB RAM. |
| Software Dependencies | No | The paper mentions that "Codes are available at https://github.com/insuhan/ntk-sketch-rf" but does not explicitly list specific software dependencies with version numbers within the text. |
| Experiment Setup | Yes | We use the Re LU network with depth L = 1. We search the ridge parameter with a random subset of training set and choose the one that achieves the best validation accuracy. We choose a convolutional network of depth L = 3 and compare CNTKSKETCH and GRADRF for various feature dimensions. For our methods and RFF, we fix the output dimension to m = 8,192 for all datasets. |