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..
Learning Human-Compatible Representations for Case-Based Decision Support
Authors: Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, Chenhao Tan
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using both synthetic data and human subject experiments in multiple classification tasks, we demonstrate that such representation is better aligned with human perception than representation solely optimized for classification. |
| Researcher Affiliation | Academia | Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen & Chenhao Tan Department of Computer Science, University of Chicago EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and data are available at https://github.com/Chicago HAI/ learning-human-compatible-representations. |
| Open Datasets | Yes | butterfly vs. moth classification from Image Net (Krizhevsky et al., 2012), and (ii) pneumonia classification based on chest X-rays (Kermany et al., 2018). |
| Dataset Splits | Yes | We generate 2000 images and randomly split the dataset into training, validation, and testing sets in a 60%:20%:20% ratio. |
| Hardware Specification | Yes | We use a computing cluster at our institution. We train our models on nodes with different GPUs including Nvidia Ge Force RTX2080Ti, Nvidia Ge Force RTX3090, Nvidia Quadro RTX 8000, and Nvidia A40. |
| Software Dependencies | No | We use the Py Torch framework (Paszke et al., 2019) and the Py Torch Lightning framework (Falcon et al., 2019) for implementation. (Specific version numbers are not provided.) |
| Experiment Setup | Yes | We use the Adam optimizer (Kingma & Ba, 2014) with learning rate 1e 4. We use a training batch size of 40 for triplet prediction, and 30 for classification. |