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..
A Kernel Perspective of Skip Connections in Convolutional Networks
Authors: Daniel Barzilai, Amnon Geifman, Meirav Galun, Ronen Basri
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 1: Left: The eigenvalues of Res CGPK (filled circles and solid lines) computed numerically for various eigenfunctions... Figure 2: The effective receptive field (ERF) induced by Res CNTK compared to that of a network and to the kernel and network with the skip connections removed. We followed (Luo et al., 2016) in computing the ERF, where the networks are first trained on CIFAR-10. |
| Researcher Affiliation | Academia | Daniel Barzilai, Amnon Geifman, Meirav Galun & Ronen Basri Weizmann Institute of Science EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about open-source code availability or a link to a code repository for its methodology. |
| Open Datasets | Yes | Figure 2: The effective receptive field (ERF) induced by Res CNTK compared to that of a network and to the kernel and network with the skip connections removed. We followed (Luo et al., 2016) in computing the ERF, where the networks are first trained on CIFAR-10. |
| Dataset Splits | No | The paper mentions training on 'CIFAR-10' in Figure 2 caption, but does not specify any dataset splits (e.g., percentages, sample counts, or specific split files) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiments. |
| Experiment Setup | Yes | Figure 1 specifies 'L = 3, q = 2, d = 4'. Figure 2 mentions 'L = 8'. Figure 3 specifies 'n = 100'. Appendix G confirms: 'Thus the kernel computed is with d = 4, q = 2, α = 1'. |