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..
NeurFlow: Interpreting Neural Networks through Neuron Groups and Functional Interactions
Authors: Tue Cao, Nhat Hoang-Xuan, Hieu Pham, Phi Le Nguyen, My Thai
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive empirical studies validate the fidelity of our proposed Neur Flow. Additionally, we showcase its utility in practical applications such as image debugging and automatic concept labeling. |
| Researcher Affiliation | Academia | 1 Institute for AI Innovation and Societal Impact (AI4LIFE), Hanoi University of Science and Technology, Hanoi, Vietnam 2 University of Florida, Gainesville, Florida, USA 3 College of Engineering & Computer Science, Vin University, Hanoi, Vietnam |
| Pseudocode | Yes | E DETAILED ALGORITHMS E.1 IDENTIFYING CORE CONCEPT NEURONS AND CONSTRUCTING NEURON CIRCUIT Algorithms 1, 2, and 3 provide detailed pseudocode for identifying core concept neurons, determining the semantic groups, and constructing the neuron circuit respectively. Algorithm 1 Identifying core concept neurons Algorithm 2 Determining semantic groups Algorithm 3 Forming neuron circuit |
| Open Source Code | Yes | 1Source code: https://github.com/tue147/neurflow |
| Open Datasets | Yes | Our experiments are performed on Res Net50 (He et al., 2016) and Goog Le Net Szegedy et al. (2015) using the ILSVRC2012 validation set (Russakovsky et al., 2015). |
| Dataset Splits | Yes | Our experiments are performed on Res Net50 (He et al., 2016) and Goog Le Net Szegedy et al. (2015) using the ILSVRC2012 validation set (Russakovsky et al., 2015). |
| Hardware Specification | No | Explanation: The paper mentions |
| Software Dependencies | No | Explanation: The paper mentions |
| Experiment Setup | Yes | Unless otherwise specified, the input parameters are τ = 16, N = 50, and k = 50, where the top 50 images with the highest activation on the target neuron are considered as its concept. |