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..
Residual Networks Behave Like Ensembles of Relatively Shallow Networks
Authors: Andreas Veit, Michael J. Wilber, Serge Belongie
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | All experiments are performed at test time on CIFAR-10 [12]. Experiments on Image Net [2] show comparable results. We train residual networks with the standard training strategy, dataset augmentation, and learning rate policy, [6]. |
| Researcher Affiliation | Academia | Andreas Veit Michael Wilber Serge Belongie Department of Computer Science & Cornell Tech Cornell University EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (link, explicit statement of release, or mention in supplementary materials) to source code for the methodology described. |
| Open Datasets | Yes | All experiments are performed at test time on CIFAR-10 [12]. Experiments on Image Net [2] show comparable results. |
| Dataset Splits | No | The paper mentions using CIFAR-10 and ImageNet but does not explicitly provide specific dataset split information for training, validation, or test sets (percentages, sample counts, or citations to predefined splits) beyond implicitly using standard datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions using 'standard training strategy, dataset augmentation, and learning rate policy' and the number of layers/modules for networks (e.g., '110-layer (54-module) residual network'), but it does not provide concrete hyperparameter values or detailed training configurations like specific learning rates, batch sizes, or optimizer settings. |