Training Quantized Neural Networks to Global Optimality via Semidefinite Programming
Authors: Burak Bartan, Mert Pilanci
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |