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 Dependency Graphs: A Causal Abstraction of Neural Networks
Authors: Yaojie Hu, Jin Tian
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show that an NDG is a causal abstraction of the corresponding neural network that unfolds the same way under causal interventions using the theory by Geiger et al. (2021a). Code is available at https://github.com/phimachine/ndg. ... Experimental results empirically support the alignment condition. ... We extract neuron dependency graphs on a diverse set of datasets and architectures to demonstrate the generality of our method. Table 1 lists the datasets and architectures |
| Researcher Affiliation | Academia | Yaojie Hu 1 Jin Tian 1 ... 1Department of Computer Science, Iowa State University, United States. Correspondence to: Yaojie Hu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Interchange intervention with a NDG |
| Open Source Code | Yes | Code is available at https://github.com/phimachine/ndg. |
| Open Datasets | Yes | We extract neuron dependency graphs on a diverse set of datasets and architectures to demonstrate the generality of our method. Table 1 lists the datasets and architectures (Le Cun et al., 1998; Socher et al., 2013; Conneau et al., 2017; Reimers & Gurevych, 2019; Welinder et al., 2010; Lu et al., 2021; Sanh et al., 2019; Dosovitskiy et al., 2021; He et al., 2021; Zhou et al., 2019; Feng et al., 2020; Liu et al., 2019; Devlin et al., 2018). |
| Dataset Splits | Yes | For the datasets with only the training set and the test set, we leave 10% of the training set for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or cloud instance types used for experiments. |
| Software Dependencies | No | The Py Torch program using Huggingface library (Wolf et al., 2019) to select layers is in Figure 4. |
| Experiment Setup | Yes | Threshold parameter α is used in Eq. (1) to extract the neuron dependency graphs. ... Threshold α is manually selected to improve interchange intervention accuracy. ... For Re LU, we select Tϕ = 1, Fϕ = 0. For sigmoid, Tϕ = , Fϕ = . |