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..
Collaborative Learning by Detecting Collaboration Partners
Authors: Shu Ding, Wei Wang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real-world datasets verify the effectiveness of our method. |
| Researcher Affiliation | Academia | Shu Ding, Wei Wang National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China EMAIL |
| Pseudocode | Yes | Algorithm 1 Adaptive Collaborative Learning with Modularity Maximization (ACLMM); Algorithm 2 Adaptive Collaborative Learning with Clustering (ACLC) |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | The MNIST dataset consists of 70K handwritten digit images in 10 classes; The CIFAR-10/100 dataset consists of 60K color images in 10/100 classes; The Federated Extended MNIST (FEMNIST) dataset is built by partitioning the data in Extended MNIST based on the writer of the digit/character. |
| Dataset Splits | Yes | The MNIST dataset consists of 70K handwritten digit images in 10 classes, which has a training set of 60K examples and a test set of 10K examples. They have 50K training images and 10K test images. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions using CNN models and ResNet-18, but does not provide specific software dependency versions (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We set N = 10 and M = 2000 for MNIST. We set N = 20 and M = 5000 for CIFAR-10 and set N = 50 and M = 20000 for CIFAR-100. We further set the number of groups K = 3 for MNIST, K = 4 for CIFAR-10, K = 7 for CIFAR-100 and K = 4 for FEMNIST. |