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..
Representer Point Selection for Explaining Deep Neural Networks
Authors: Chih-Kuan Yeh, Joon Kim, Ian En-Hsu Yen, Pradeep K. Ravikumar
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform a number of experiments with multiple datasets and evaluate our method s performance and compare with that of the influence functions. |
| Researcher Affiliation | Academia | Chih-Kuan Yeh Joon Sik Kim Ian E.H. Yen Pradeep Ravikumar Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 EMAIL |
| Pseudocode | No | The paper describes the steps of the algorithm in prose, but does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | Source code available at github.com/chihkuanyeh/Representer_Point_Selection. |
| Open Datasets | Yes | We perform a number of experiments with multiple datasets and evaluate our method s performance... on CIFAR-10 dataset [15]... in Animals with Attributes (Aw A) dataset [18]. |
| Dataset Splits | No | The paper mentions training data and test data, but does not explicitly provide details about a separate validation split, such as percentages or counts, or cross-validation setup. |
| Hardware Specification | No | The paper does not specify any particular hardware components like CPU models, GPU models, or memory specifications used for the experiments. |
| Software Dependencies | No | The paper mentions using specific models like VGG-16 and Resnet-50, and optimization methods like SGD and LBFGS, but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The L2 weight decay is set to 1e-2 for all methods for fair comparison. We first solve (4) with loss Lsoftmax(Φ(xi, Θ), Φ(xi, Θgiven)) for λ = 0.001, and then calculate Φ(xt, Θ ) = Pn i=1 k(xt, xi, αi) as in (2) for all train and test points. |