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..
Aggregating Crowd Wisdom with Side Information via a Clustering-based Label-aware Autoencoder
Authors: Li'ang Yin, Yunfei Liu, Weinan Zhang, Yong Yu
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world tasks demonstrate the significant improvement of CLA compared with the state-of-the-art aggregation algorithms. |
| Researcher Affiliation | Academia | Li ang Yin, Yunfei Liu, Weinan Zhang, Yong Yu Shanghai Jiao Tong University, No.800 Dongchuan Road, Shanghai, China EMAIL |
| Pseudocode | No | The paper describes algorithms and processes textually but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Demo code is at https://github.com/coverdark/cla demo |
| Open Datasets | Yes | Reuters contains a document categorization task... [Rodrigues et al., 2017]. CUB-200-2010 dataset contains tasks to label local characteristics for 6,033 bird images [Welinder et al., 2010]. |
| Dataset Splits | Yes | Hyperparameter search adopts a similar manner with LAA by splitting a dataset into a training set and a validation set [Yin et al., 2017]. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' but does not specify a version number or other key software dependencies with their versions. |
| Experiment Setup | Yes | Sampling time T = 5. ... Here the learning rate is 0.001. Training is stable and usually achieves desirable inference accuracy after 1,500 epochs. |