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..
Training Quantized Neural Networks to Global Optimality via Semidefinite Programming
Authors: Burak Bartan, Mert Pilanci
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present numerical examples to illustrate the effectiveness of our method. |
| Researcher Affiliation | Academia | Department of Electrical Engineering, Stanford University, CA, USA. |
| Pseudocode | Yes | Algorithm 1 Sampling algorithm for quantized neural networks |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about releasing its own source code for the described methodology. It mentions using third-party tools like CVXPY, SCS, and PyTorch. |
| Open Datasets | Yes | The dataset is the binary classification breast-cancer dataset and has n = 228 training samples and 58 test samples and the samples are d = 9 dimensional. Figure 2 shows the classification accuracy against time for various methods which we describe below. The regularization coefficient β is picked for each method separately by searching the value that yields the highest accuracy and the resulting β values are provided in the captions of the figures. [...] Figure 3 shows results for the UCI repository dataset ionosphere . This is a binary classification dataset with n = 280 training samples and 71 test samples. The samples are d = 33 dimensional. The experiment setting is similar to Figure 2 with the main difference that the number of neurons is 10 times higher (i.e., m = 2500). |
| Dataset Splits | No | The paper specifies training and test sample counts for the datasets used but does not mention a distinct validation split. |
| Hardware Specification | Yes | The experiments have been carried out on a Mac Book with 2.2 GHz 6-Core Intel Core i7 processor and 16 GB of RAM. |
| Software Dependencies | No | The paper mentions software like CVXPY, SCS, and PyTorch, but does not provide specific version numbers for these dependencies within the text. |
| Experiment Setup | Yes | The regularization coefficient is β = 10 4. [...] We fix the second layer weights to 1/m during training. [...] The number of neurons is m = 250 and the regularization coefficient is β = 0.1 for the SDP based method and β = 0.1 for the backpropagation. [...] The number of neurons is 10 times higher (i.e., m = 2500) and the regularization coefficient is β = 10 for the SDP based method, β = 10 6 for backpropagation. |