Proximity Preserving Binary Code Using Signed Graph-Cut
Authors: Inbal Lavi, Shai Avidan, Yoram Singer, Yacov Hel-Or4535-4544
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Proximity Preserving Code on several public datasets: MNIST (Deng 2012), CIFAR-10 (Krizhevsky, Nair, and Hinton 2014), and Label Me (Russell et al. 2008). |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering, Tel-Aviv University, Israel 2Department of Computer Science, Princeton University, NJ, USA 3School of Computer Science, The Interdisciplinary Center, Israel |
| Pseudocode | Yes | Algorithm 1 Vector Update (b, W) and Algorithm 2 Bit Update (b, W) |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of their code. |
| Open Datasets | Yes | We evaluate Proximity Preserving Code on several public datasets: MNIST (Deng 2012), CIFAR-10 (Krizhevsky, Nair, and Hinton 2014), and Label Me (Russell et al. 2008). |
| Dataset Splits | No | CIFAR-10 ... It is split into 59,000 images in the training set and 1000 in the test set. MNIST ... The dataset is split into a training set of 69,000 samples and a test set of 1,000 samples. No explicit mention of a validation set or split for these datasets. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are provided. |
| Software Dependencies | No | We use kernel SVM (Scholkopf and Smola 2001) with Gaussian kernels to classify the points {xi} into 1, but any standard classifier can be applied similarly. No specific version numbers for SVM library or any other software components are provided. |
| Experiment Setup | No | No specific hyperparameters (e.g., learning rate, batch size, epochs, optimizer settings) or detailed training configurations are provided for the proposed PPC method. |