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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Equals Shallow for ReLU Networks in Kernel Regimes
Authors: Alberto Bietti, Francis Bach
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Numerical experiments We now present numerical experiments on synthetic and real data to illustrate our theory. Our code is available at https://github.com/albietz/deep_shallow_kernel. Synthetic experiments. We consider randomly sampled inputs on the sphere S3 in 4 dimensions, and outputs generated according to the following target models... MNIST and Fashion-MNIST. In Table 1, we consider the image classification datasets MNIST and Fashion-MNIST, which both consist of 60k training and 10k test images... |
| Researcher Affiliation | Academia | Alberto Bietti NYU EMAIL Francis Bach Inria EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/albietz/deep_shallow_kernel. |
| Open Datasets | Yes | MNIST and Fashion-MNIST. In Table 1, we consider the image classification datasets MNIST and Fashion-MNIST, which both consist of 60k training and 10k test images of size 28x28 with 10 output classes. |
| Dataset Splits | Yes | We train on random subsets of 50k examples and use the remaining 10k examples for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions that code is available, but does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | The regularization parameter λ is optimized on 10 000 test datapoints on a logarithmic grid. (...) We evaluate one-versus-all classifiers obtained by using kernel ridge regression by setting y = 0.9 for the correct label and y = 0.1 otherwise. (...) We train on random subsets of 50k examples and use the remaining 10k examples for validation. |