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..
Neuron Shapley: Discovering the Responsible Neurons
Authors: Amirata Ghorbani, James Y. Zou
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical: our systematic experiments discover several interesting findings, including the phenomenon that a small number of neurons are critical to different aspects of a network s performance, e.g. accuracy, fairness, robustness. This facilitates both model interpretation and repair. |
| Researcher Affiliation | Academia | Amirata Ghorbani Department of Electrical Engineering Stanford University Stanford, CA 94025 EMAIL; James Zou Department of Biomedical Data Science Stanford University Stanford, CA 94025 EMAIL |
| Pseudocode | Yes | Algorithm 1 Truncated Multi Armed Bandit Shapley |
| Open Source Code | Yes | Code is available on Github at https://github.com/amiratag/neuronshapley |
| Open Datasets | Yes | First is the Inception-v3 [44] architecture trained on the ILSVRC2012 (a.k.a Image Net) [36] dataset... The second model is the Squeeze Net [16] architecture... trained on the celeb A [25] dataset |
| Dataset Splits | Yes | We divide the released Image Net validation set into two parts (25000 images each) to serve as validation and test sets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We set k = 100 to detect the top-100 important filters. We run the original MC-Shapley algorithm and our TMAB-Shapley algorithm on the Squeezenet model (δ = 0.05, = 10 4). We use iterative PGD attack [22, 28] as an adversary... we use 1 perturbations with size = 16/255. |