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..
Stacking With Auxiliary Features
Authors: Nazneen Fatema Rajani, Raymond J. Mooney
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experimental Results |
| Researcher Affiliation | Academia | Nazneen Fatema Rajani Department of Computer Science University of Texas at Austin EMAIL Raymond J. Mooney Department of Computer Science University of Texas at Austin EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We use SWAF to demonstrate new state-of-the-art results on two of the three tasks... The first two are in NLP and part of the NIST Knowledge Base Population (KBP) challenge Cold Start Slot-Filling (CSSF) and Entity Discovery and Linking (EDL) [Ji et al., 2016]. The third task is in computer vision and part of the Image Net 2015 challenge [Russakovsky et al., 2015] Object Detection from images. |
| Dataset Splits | Yes | A small random sample of the training set (10%) was used as validation data to set the hyper-parameters for each of the tasks. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments are provided. |
| Software Dependencies | No | The paper mentions software like 'Caffe system' and 'word2vec model', but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For the two NLP tasks, we used an L1 regularized linear SVM weighted by the number of instances for each class, as a meta-classifier for stacking. For the object detection task, we used an SVM with an RBF kernel. ... we first embed words into vectors by training a word2vec model [Mikolov et al., 2013] with word vector dimension of 100, window-size set to 8, 10 negative samples and 10 iterations on Free Base. |