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 [1].
Model Fusion for Personalized Learning
Authors: Thanh Chi Lam, Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Theoretical Analysis; 5. Experiments; The evaluation shows that the personalized model ο¬ne-tuned with limited data performs competitively to a model built on extensive data. |
| Researcher Affiliation | Collaboration | 1National University of Singapore 2AWS AI Labs, Amazon 3Massachusetts Institute of Technology. |
| Pseudocode | No | The paper describes its methods in text and does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code: https://github.com/zevergreenz/model_fusion_for_personalized_learning |
| Open Datasets | Yes | 5.2. MNIST Dataset (Le Cun et al., 2010), Le Cun, Y., Cortes, C., and Burges, C. Mnist handwritten digit database. ATT Labs [Online]. Available: http://yann. lecun. com/exdb/mnist, 2, 2010. and 5.3. Movie-Len Dataset (Harper & Konstan, 2015), Harper, F. M. and Konstan, J. A. The Movie Lens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TIIS), 5(4):1 19, 2015. |
| Dataset Splits | No | The paper mentions training and testing sets for its experiments but does not explicitly describe a validation dataset split or how it was used. |
| Hardware Specification | No | The paper describes the experimental setup and results but does not provide specific hardware details such as GPU/CPU models or memory specifications used for running experiments. |
| Software Dependencies | No | The paper mentions that code is available but does not specify any software dependencies with version numbers (e.g., specific Python, PyTorch, or TensorFlow versions) that would be needed for replication. |
| Experiment Setup | No | The paper describes model architectures (e.g., '1-layer neural net with 100 hidden neurons, [1-100-1], with Re LU activation') and the few-shot learning setup, but does not provide specific hyperparameter values like learning rates, batch sizes, or optimizer settings. |