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..
Globally Optimal Training of Neural Networks with Threshold Activation Functions
Authors: Tolga Ergen, Halil Ibrahim Gulluk, Jonathan Lacotte, Mert Pilanci
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We corroborate our theoretical results with various numerical experiments. |
| Researcher Affiliation | Academia | Department of Electrical Engineering Stanford University Stanford, CA 94305, USA |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about open-source code availability or links to code repositories. |
| Open Datasets | Yes | For this experiment, we use CIFAR-10 (Krizhevsky et al., 2014), MNIST (Le Cun), and the datasets in the UCI repository (Dua & Graff, 2017) which are preprocessed as in Fern andez-Delgado et al. (2014). |
| Dataset Splits | No | We also use the 80% 20% splitting ratio for the training and test sets of the UCI datasets. |
| Hardware Specification | Yes | We ο¬rst note that all of the experiments in the paper are run on a single laptop with Intel(R) Core(TM) i7-7700HQ CPU and 16GB of RAM. |
| Software Dependencies | No | The paper mentions using "PyTorch s (Paszke et al., 2019)" but does not specify a version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | We also tune the learning rate of STE by performing a grid search on the set {5e 1, 1e 1, 5e 2, 1e 2, 5e 3, 1e 3}. As illustrated in Figure 5, the non-convex training heuristic STE fails to achieve the global minimum obtained by our convex training algorithm for 5 different initialization trials. |