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 | Archive PDF | Plain Text | 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. |