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].
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. |