Combining Logical Abduction and Statistical Induction: Discovering Written Primitives with Human Knowledge
Authors: Wang-Zhou Dai, Zhi-Hua Zhou
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we report two experimental results of LASIN on 3 real handwritten characters datasets. |
| Researcher Affiliation | Academia | Wang-Zhou Dai and Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China {daiwz, zhouzh}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1: LASIN |
| Open Source Code | No | The paper does not provide explicit statements about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | MNIST (Le Cun et al. 2001): This dataset consists of 28 28 binary images with 60,000 training and 10,000 test instances. and Omniglot (Lake, Salakhutdinov, and Tenenbaum 2015): Omniglot dataset consists of 105 105 binary images across 1628 classes with just 20 images per class. |
| Dataset Splits | Yes | MNIST (Le Cun et al. 2001): This dataset consists of 28 28 binary images with 60,000 training and 10,000 test instances. and The performance are evaluated with 5-fold cross-validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'mlpack toolbox (Curtin et al. 2013)' and 'SWIProlog (Wielemaker et al. 2012)' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The dictionary sizes are set at |D| = 20, 50, 100, 200, respectively. These sizes are not very large because we believe the effective dimension of handwritten characters should be small, involving some different strokes, their combinations and spacial relations. The hyper-parameters (turn limit and error threshold) of Algorithm 1 in the experiments are determined by cross-validation on training data. |