Approximation and Learning with Deep Convolutional Models: a Kernel Perspective
Authors: Alberto Bietti
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | These achieve good empirical performance on standard vision datasets, while providing a precise description of their functional space that yields new insights on their inductive bias. and Table 1: Cifar10 test accuracy with 2-layer convolutional kernels with 3x3 patches and pooling/downsampling sizes [2,5], with different choices of patch kernels κ1 and κ2. |
| Researcher Affiliation | Academia | Alberto Bietti Center for Data Science, New York University alberto.bietti@nyu.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/albietz/ckn_kernel. |
| Open Datasets | Yes | We consider classification on Cifar10 dataset, which consists of 50k training images and 10k test images with 10 different output categories. |
| Dataset Splits | No | The paper states 'Cifar10 dataset, which consists of 50k training images and 10k test images', but does not explicitly mention a separate validation split or its size. |
| Hardware Specification | Yes | The computation of kernel matrices is distributed on up to 1000 cores on a cluster consisting of Intel Xeon processors. |
| Software Dependencies | No | The paper mentions 'C++', 'Eigen library', and 'Py Torch implementation' but does not provide specific version numbers for any of these software components. |
| Experiment Setup | Yes | We report the test accuracy for a fixed regularization parameter λ = 10 8 (we note that the performance typically remains the same for smaller values of λ). |