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 Deep Learning Dataloader with Shared Data Preparation
Authors: jian xie, Jingwei Xu, Guochang Wang, Yuan Yao, Zenan Li, Chun Cao, Hanghang Tong
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed JOADER , showing a greater versatility and superiority of training speed improvement (up to 200% on Res Net18) without affecting the accuracy. |
| Researcher Affiliation | Academia | 1Nanjing University 2University of Illinois Urbana-Champaign |
| Pseudocode | No | The paper describes algorithms and data structures through textual descriptions and diagrams, but no formal pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper states that a prototype named JOADER was implemented, but it does not provide any concrete access information for its source code. |
| Open Datasets | Yes | In this section, we evaluate JOADER on Image Net with the family of Res Net models. |
| Dataset Splits | No | The paper evaluates on ImageNet but does not explicitly provide specific training/validation/test dataset splits (percentages, counts, or detailed methodology for splitting). |
| Hardware Specification | Yes | The experiments were conducted on a GPU server with two Intel Xeon Gold 5118 CPUs @ 2.30GHz (24 physical cores and 48 threads), 500GB RAM, and 6 TITAN RTX GPUs. The server ran Ubuntu 18.04 with GNU/Linux kernel 4.15.0. The disk is Symbios Logic Mega RAID SAS-3 3316 of 1GB/s read speed. |
| Software Dependencies | Yes | The evaluated models are the basic models with their default settings in torchvision [1], and trained on top of the Py Torch 1.6.0 DL framework. The server ran Ubuntu 18.04 with GNU/Linux kernel 4.15.0. |
| Experiment Setup | Yes | The evaluated models are the basic models with their default settings in torchvision [1], and trained on top of the Py Torch 1.6.0 DL framework. In this experiment, we start training multiple Res Net18 models at the same time but with different hyper-parameters. |