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..
Neural Kernels Without Tangents
Authors: Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Jonathan Ragan-Kelley, Ludwig Schmidt, Benjamin Recht
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we show a correlation in test error between neural network architectures and the associated kernels. We construct a simple neural network architecture using only 3 3 convolutions, 2 2 average pooling, Re LU, and optimized with SGD and MSE loss that achieves 96% accuracy on CIFAR10, and whose corresponding compositional kernel achieves 90% accuracy. |
| Researcher Affiliation | Academia | 1University of California, Berkeley 2Massachusetts Institute of Technology. |
| Pseudocode | Yes | Algorithm 1 Compositional Kernel |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing their code for the described methodology, nor does it provide a direct link to a source code repository. |
| Open Datasets | Yes | We then present comparison results between neural networks, NTKs, and compositional kernels on a variety of datasets, including MNIST, CIFAR-10 (Krizhevsky (2009)), CIFAR-10.1 (Recht et al. (2019)), CIFAR-100 (Krizhevsky (2009)) and 90 UCI datasets (Fern andez-Delgado et al. (2014)). |
| Dataset Splits | Yes | Table 3 compares the performance of neural networks with various depths and their corresponding compositional kernels on both the 10,000 test images from CIFAR-10 and the additional 2,000 harder test images from CIFAR-10.1. We compute the optimal hyperparameters for each dataset (for both NTK and Gaussian kernel) by averaging performance over four cross-validation folds. |
| Hardware Specification | Yes | We implemented all the convolutional kernels in the tensor comprehensions framework (Vasilache et al., 2018) and executed them on V100 GPUs using Amazon Web Services (AWS) P3.16xlarge instances. |
| Software Dependencies | No | The paper mentions implementing kernels in the 'tensor comprehensions framework' and refers to a publication (Vasilache et al., 2018), but it does not provide specific version numbers for this framework or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | We train all the Myrtle CNNs on CIFAR-10 using SGD and the mean squared error (MSE) loss with multi-step learning rate decay. The exact hyperparameters are provided in the appendix. |