Using Convolutional Neural Networks to Analyze Function Properties from Images
Authors: Yoad Lewenberg, Yoram Bachrach, Ian Kash, Peter Key
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose algorithms for randomly constructing functions with convexity or symmetry properties, and use the images generated by these algorithms to train our network. Our system achieves a high accuracy on this task... Empirical Results: We used an 80%-20% train-test partitioning on an image subset to determine the performance of the CNN in classifying the images. |
| Researcher Affiliation | Collaboration | Yoad Lewenberg The Hebrew University of Jerusalem, Israel Yoram Bachrach Microsoft Research Cambridge, UK Ian Kash Microsoft Research Cambridge, UK Peter Key Microsoft Research Cambridge, UK |
| Pseudocode | No | The paper describes algorithms in paragraph text but does not contain structured pseudocode or algorithm blocks (e.g., labeled "Algorithm 1"). |
| Open Source Code | No | The paper does not provide concrete access to source code, such as a specific repository link or an explicit code release statement. |
| Open Datasets | No | The paper describes generating its own dataset ("We provide algorithms for generating random functions... and generate a large image dataset...") but does not provide concrete access information (link, DOI, repository name, or formal citation for public availability) for this dataset. |
| Dataset Splits | Yes | We used an 80%-20% train-test partitioning on an image subset to determine the performance of the CNN in classifying the images. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using "Caffe framework (Jia et al. 2014)" but does not provide specific version numbers for Caffe or any other software dependencies. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations. |