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..
Learning Compact Neural Networks with Regularization
Authors: Samet Oymak
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To support our theoretical findings, we present numerical performance of sparsity and convolutional constraints for neural network training. We consider synthetic simulations where o is a vector of all ones and weight matrix W Rh p is sparse or corresponds to a CNN. |
| Researcher Affiliation | Collaboration | University of California, Riverside, CA, USA. Work done at The Voleon Group, Berkeley, CA, USA. |
| Pseudocode | No | The paper describes algorithms (e.g., Projected Gradient Descent) and their iterations but does not provide them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the methodology, nor does it include a link to a code repository. |
| Open Datasets | No | We consider synthetic simulations where o is a vector of all ones and weight matrix W Rh p is sparse or corresponds to a CNN. ... We generate W matrices with exactly s nonzero entries at each row and nonzero pattern is distributed uniformly at random. ... We generate kernel entries with i.i.d. N(0, p/hb) and the random matrix Z with i.i.d. N(0, p/bk) entries. The paper does not provide access information (link, citation, etc.) for a publicly available or open dataset, as it uses synthetic data generated for the experiments. |
| Dataset Splits | No | For training, we use n data points which varies from 100 to 1000. Test error is obtained by averaging ntest = 1000 independent data points. The paper mentions training and test sets but does not specify a validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We set the learning rate to µ = 5. ... We picked p = 80, h = 20 and s = p/10 = 8. For training, we use n data points which varies from 100 to 1000. ... Problem parameters are input dimension p = 81, kernel width b = 15, stride s = 6, number of kernels k = 4 and learning rate µ = 1. |