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..
Development of JavaScript-based deep learning platform and application to distributed training
Authors: Masatoshi Hidaka, Ken Miura, Tatsuya Harada
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we demonstrate their practicality by training VGGNet in a distributed manner using web browsers as the client. |
| Researcher Affiliation | Academia | Masatoshi Hidaka, Ken Miura & Tatsuya Harada Department of Information Science and Technology The University of Tokyo 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan EMAIL |
| Pseudocode | Yes | Figure 1: Example of forward calculation of fully-connected layer using Sushi2 |
| Open Source Code | Yes | The source code is provided as open-source software1. Download code from https://github.com/mil-tokyo |
| Open Datasets | Yes | We evaluated them by training LeNet with MNIST dataset (Le Cun et al., 1998b). |
| Dataset Splits | No | While the paper mentions using the MNIST dataset and shows 'mnist train' and 'mnist test' in a configuration file example (Figure 2), it does not explicitly provide details about a specific validation dataset split. |
| Hardware Specification | Yes | Table 2: Hardware used for the experiments. GPU: AMD FirePro S9170, NVIDIA K80. CPU: Intel Core i7-5930K, Intel Xeon E5-2690 v3. |
| Software Dependencies | Yes | Firefox (version 32) and node.js (version 4.3.0) are used as the JavaScript execution environment. |
| Experiment Setup | Yes | The network structure is based on Le Cun et al. (1998a), which contains two convolutional layers and two fully-connected layers. The batch size is 64. ... The optimization method is momentum SGD. ... The batch size is 256 according to (Simonyan & Zisserman, 2014a). |