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..
S$Ω$I: Score-based O-INFORMATION Estimation
Authors: Mustapha Bounoua, Giulio Franzese, Pietro Michiardi
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments validate our approach on synthetic data, and demonstrate the effectiveness of SΩI in the context of a real-world use case. |
| Researcher Affiliation | Collaboration | 1Ampere Software Technology, France 2Department of Data Science, Eurecom, France. |
| Pseudocode | Yes | Algorithm 1 SΩI Training step |
| Open Source Code | Yes | We provide code-base for SΩI implementation at 1. The training of SΩI is carried out using Adam optimizer (Kingma & Ba, 2015). 1https://github.com/Mustapha Bounoua/soi |
| Open Datasets | Yes | We consider the Visual Behavior project, which used the Allen Brain Observatory to collect a highly standardized dataset consisting of recordings of neural activity in mice (Allen-Institute, 2022). |
| Dataset Splits | No | For each experiment, we use 100k samples for training the various neural estimators, and 10k samples at inference time, to estimate O-INFORMATION. The paper explicitly mentions training and testing sets, but a separate validation split is not specified. |
| Hardware Specification | No | No specific hardware details such as GPU or CPU models, memory, or cluster specifications are provided in the paper. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and frameworks like 'VP-SDE formulation', but it does not specify exact version numbers for any of them (e.g., PyTorch 1.9, Python 3.8). |
| Experiment Setup | Yes | For our method SΩI, we use the VP-SDE formulation (Song et al., 2021) and learn a unique denoising network to estimate the various score terms. The denoiser is a simple, stacked multilayer perceptron (MLP) with skip connections, adapted to the input dimension. We apply importance sampling (Huang et al., 2021; Song et al., 2021) at both training and inference time. Finally, we use 10-sample Monte Carlo estimates for computing integrals. (Table 1 provides hyperparameters like Width, Time embed, Batch size, Lr, Iterations). |