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..
Balancing Appearance and Context in Sketch Interpretation
Authors: Yale Song, Randall Davis, Kaichen Ma, Dana L. Penney
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We calibrate the utility of different forms of context, describing experiments with Conditional Random Fields trained and tested using a variety of features. |
| Researcher Affiliation | Collaboration | Yale Song1, Randall Davis2, Kaichen Ma3, Dana L. Penney4 1Yahoo Research, 2Massachusetts Institute of Technology, 3Google Inc., 4Lahey Hospital and Medical Center |
| Pseudocode | Yes | Algorithm 1 Detecting Overwriting and Augmentation; Algorithm 2 Repair Strategy for Under-segmentation; Algorithm 3 Repair Strategy for Over-segmentation |
| Open Source Code | No | The paper does not provide any statements about releasing source code or links to a code repository. |
| Open Datasets | No | We used a set of 2,024 clock drawings collected from clinical facilities: 1,654 clocks drawn by healthy participants and 370 clocks randomly selected from mildly cognitively impaired participants. We have accumulated a database of several thousand tests from both healthy and cognitively impaired subjects, a unique dataset of real-world drawing behavior. |
| Dataset Splits | Yes | To avoid over-fitting, we used an l2 regularization with its scale term set at 0.01, chosen based on 10-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'boosted logistic regression classifier' and 'sketched symbol recognizer of [Ouyang and Davis, 2009b]' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | To avoid over-fitting, we used an l2 regularization with its scale term set at 0.01, chosen based on 10-fold cross validation. |