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..
DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning
Authors: Tian Zhao, Xiao Bing Huang, Yu Cao
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated Deep DSL with a number of popular DL networks. Our experiments show that the compiled programs have very competitive runtime performance and memory ef๏ฌciency compared to the existing libraries. |
| Researcher Affiliation | Academia | Tian Zhao & Xiao Bing Huang Department of Computer Science University of Wisconsin Milwaukee Milwaukee, WI, USA EMAIL Yu Cao Department of Computational Neuroscience The University of Massachusetts, Lowell Lowell, MA, USA EMAIL |
| Pseudocode | No | The paper includes code examples in Scala (Figure 2 and subsequent code blocks) but does not provide structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Deep DSL is available at https://github.com/deepdsl/deepdsl. |
| Open Datasets | Yes | All our tests are trained with Image Net images that have been resized to 224 by 224 |
| Dataset Splits | No | The paper mentions training and testing data but does not provide specific details on validation dataset splits, percentages, or sample counts. |
| Hardware Specification | Yes | The tests are run on a server with a single NVIDIA Tesla K40C GPU equipped with 12 gigabytes of memory. The server runs the Cent OS 7 Linux distribution. |
| Software Dependencies | Yes | Deep DSL uses the JCuda 0.8.0RC binding that runs against CUDA 8.0.279. |
| Experiment Setup | Yes | Figure 2 shows the complete implementation for compiling a program to train and test Lenet... val solver = Train('lenet', 1000, 10, 0.01f, 0.9f, 0.0005f, 0) // output file, train and test iteration, learn rate, momentum, decay, gradient cropping (0 means none) |