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..
CoRTX: Contrastive Framework for Real-time Explanation
Authors: Yu-Neng Chuang, Guanchu Wang, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai, Xia Hu
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three real-world datasets further demonstrate the efficiency and efficacy of our proposed Co RTX framework. |
| Researcher Affiliation | Collaboration | 1Rice University, 2Meta Platforms, Inc. EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Real-Time Explainer Training with Co RTX |
| Open Source Code | Yes | Our source code is available at: https://github.com/ynchuang/Co RTX-720 |
| Open Datasets | Yes | Our experiments consider two tabular datasets: Census (Dua & Graff, 2017) with 13 features, Bankruptcy (Liang et al., 2016) with 96 features, and one image dataset: CIFAR-10 (Krizhevsky et al., 2009) with 32 x 32 pixels. |
| Dataset Splits | Yes | Census Income: A collection of human social information with 26048 samples for training and validating; and 6513 samples for testing. Bankruptcy: A financial dataset contains 5455 samples of companies in the training set and validating set; and 1364 instances for the testing set. CIFAR-10: An image dataset with 60000 images in 10 different classes, where each image is composed of 32 x 32 pixels. We follow the original dataset division on training, validating, and testing process. |
| Hardware Specification | Yes | Device Attribute Value Computing infrastructure GPU GPU model Nvidia-A40 GPU number 1 GPU Memory 46068 MB |
| Software Dependencies | No | The paper mentions 'Deep CTR || package (Shen, 2017)' and 'Captum' as tools used, but does not provide specific version numbers for these or other key software components used in their implementation of CoRTX, beyond a publication year for DeepCTR's description. |
| Experiment Setup | Yes | Table 2: Hyper-parameters and model structures settings in Co RTX. The explanation encoder and the explanation head are designed with the model structures and are learned with the hyper-parameters in Table 2. |