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 from Crowds by Modeling Common Confusions
Authors: Zhendong Chu, Jing Ma, Hongning Wang5832-5840
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both synthesized and real-world benchmarks demonstrate the effectiveness of our proposed common noise adaptation solution. |
| Researcher Affiliation | Academia | Zhendong Chu, Jing Ma, Hongning Wang Department of Computer Science, University of Virginia EMAIL |
| Pseudocode | No | The paper describes its model and learning framework but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The CIFAR-10 dataset is generated based on the CIFAR-10 image classification dataset (Krizhevsky, Hinton et al. 2009). [...] Label Me (Rodrigues and Pereira 2018; Russell et al. 2008) is an image classification dataset [...] Music (Rodrigues, Pereira, and Ribeiro 2014) is a music genre classification dataset |
| Dataset Splits | Yes | On the Synthetic dataset, we completely synthesized everything. [...] a 8,000-instance training set, a 1,000-instance validation set and a 1,000-instance testing set. The CIFAR-10 dataset [...] split into a 40,000-instance training set, a 10,000-instance validation set and a 10,000-instance testing set. |
| Hardware Specification | Yes | We implement our framework with Py Torch, and run it on a Cent OS system with one NVIDIA 2080Ti GPU with 10 GB memory. |
| Software Dependencies | No | The paper mentions "Py Torch" and "Adam optimizer (Kingma and Ba 2014)" but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We trained the network using the Adam optimizer (Kingma and Ba 2014) with default parameters and learning rate searched from {0.02, 0.01, 0.005}. The dimension of annotator and instance embedding is chosen from {20, 40, 60, 80}. The regularization term λ is searched from {10-4, 10-5, 10-6}. |