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..
Cost-Effective Interactive Attention Learning with Neural Attention Processes
Authors: Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee, Eunho Yang, Sung Ju Hwang
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate IAL on various time-series datasets from multiple domains (healthcare, real-estate, and computer vision) on which it significantly outperforms baselines with conventional attention mechanisms, or without cost-effective reranking, with substantially less retraining and human-model interaction cost. |
| Researcher Affiliation | Collaboration | 1Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea 2Yonsei University College of Medicine, Seoul, South Korea 3AITRICS, Seoul, South Korea. |
| Pseudocode | Yes | Algorithm 1 Interactive Attention Learning Framework |
| Open Source Code | Yes | The source codes and all datasets used for our experiments are publicly available at https://github.com/jayheo/IAL. |
| Open Datasets | Yes | The source codes and all datasets used for our experiments are publicly available at https://github.com/jayheo/IAL. |
| Dataset Splits | Yes | For all datasets, we generate train/valid/test splits with the ratio of 70%:10%:20%. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, or memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper states 'Please see supplementary file for more details of the datasets, network configurations, and hyperparameters.' The main text does not include specific hyperparameter values or detailed training configurations. |