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..
Explainable Models with Consistent Interpretations
Authors: Vipin Pillai, Hamed Pirsiavash2431-2439
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform all our experiments on Image Net (Deng et al. 2009) and MS-COCO (Lin et al. 2014) datasets. Tables 1 and 2 show the results using the evaluation metrics from section 4.3 on the Image Net and MS-COCO datasets respectively. |
| Researcher Affiliation | Academia | Vipin Pillai, Hamed Pirsiavash University of Maryland, Baltimore County |
| Pseudocode | No | The paper describes its method verbally and mathematically but does not include a structured pseudocode block or algorithm. |
| Open Source Code | Yes | The code and models are publicly available. |
| Open Datasets | Yes | We perform all our experiments on Image Net (Deng et al. 2009) and MS-COCO (Lin et al. 2014) datasets. |
| Dataset Splits | Yes | For evaluation, we use the validation set of 50k images for Image Net and 40k images for MS-COCO dataset. |
| Hardware Specification | Yes | We use Py Torch (Paszke et al. 2019) along with Nvidia Titan RTX and 2080Ti GPUs for training and evaluating our models. |
| Software Dependencies | No | The paper mentions using Py Torch but does not provide specific version numbers for PyTorch or any other software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | For training the models on the Image Net dataset, we use SGD with a learning rate of 0.1 for Res Net18 and 0.01 for Alex Net decayed by 0.1 every 30 epochs. We set the λ hyperparameter in Eq (5) to 25 for the Image Net experiments and 1 for the MS-COCO experiments respectively. |