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..
Skip Connections Eliminate Singularities
Authors: Emin Orhan, Xaq Pitkow
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | These hypotheses are supported by evidence from simplified models, as well as from experiments with deep networks trained on real-world datasets. |
| Researcher Affiliation | Academia | A. Emin Orhan Xaq Pitkow EMAIL EMAIL Baylor College of Medicine & Rice University |
| Pseudocode | No | The paper does not contain any sections, figures, or blocks explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not include any explicit statements about releasing source code or provide a link to a code repository. |
| Open Datasets | Yes | The networks were trained on the CIFAR-100 dataset (with coarse labels) using the Adam optimizer (Kingma & Ba, 2014) with learning rate 0.0005 and a batch size of 500. |
| Dataset Splits | No | The paper states 'We used the standard splits of the data into training and test sets.' but does not explicitly mention or provide details for a separate validation set split, its size, or how it was used. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU models, CPU types, or cloud computing instance details used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer (Kingma & Ba, 2014)' but does not specify any version numbers for Adam or any other software libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | The networks were trained on the CIFAR-100 dataset (with coarse labels) using the Adam optimizer (Kingma & Ba, 2014) with learning rate 0.0005 and a batch size of 500. |