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..
lpNTK: Better Generalisation with Less Data via Sample Interaction During Learning
Authors: Shangmin Guo, Yi Ren, Stefano V Albrecht, Kenny Smith
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run our experiment using the Res Net-18 model (He et al., 2016) and a subset of the CIFAR-10 dataset (Krizhevsky et al., 2009): we select 4096 samples randomly from all the ten classes, giving a set of 40960 samples in total. On this dataset, we first track the learning difficulty of samples through a single training run of the model. Then, we randomly split it into 4096 X subsets where X {1, 4, 16, 256, 1024}, and train a model on each of these subsets. |
| Researcher Affiliation | Academia | University of Edinburgh, University of British Columbia, |
| Pseudocode | Yes | Algorithm 1: Correlation between Learning Difficulty on Size N and Size X; Algorithm 2: Predict forgetting events with a variant of lp NTK Îș; Algorithm 3: Farthest Point Clustering with lp NTK |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We run our experiment using the Res Net-18 model (He et al., 2016) and a subset of the CIFAR-10 dataset (Krizhevsky et al., 2009): we select 4096 samples randomly from all the ten classes, giving a set of 40960 samples in total. |
| Dataset Splits | No | The paper mentions using a 'validation set' to select the best parameters: '1) fit the model on a given benchmark, and select the parameters w which has the best performance on validation set'. However, it does not provide specific details on the size, proportion, or methodology of this validation split for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions models like 'Res Net-18' and 'Le Net-5' and optimization algorithms like 'SGD' but does not specify version numbers for any software libraries or frameworks (e.g., PyTorch, TensorFlow, scikit-learn) used in the implementation. |
| Experiment Setup | Yes | In all runs, we use the same hyperparameters and train the network with the same batch size. ... this setting runs on MNIST with N = 4096, X {1, 4, 16, 64, 256, 1024}, learning rate is set to 0.1, and batch size is 128 ... this setting runs on MNIST with N = 4096, X {1, 4, 16, 64, 256, 1024}, learning rate is set to 0.001, and batch size is 256 |