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..
Mean-field Chaos Diffusion Models
Authors: Sungwoo Park, Dongjun Kim, Ahmed Alaa
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. Empirical Study This section provides a numerical validation of the efficacy of integrating MFT into the SGM framework, particularly in extreme scenarios of large cardinality, where previous works struggle to achieve robust performance. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering and Computer Sciences, UC Berkeley 2Department of Computer Science, Stanford 3UCSF. |
| Pseudocode | Yes | A.9.1. TRAINING MEAN-FIELD CHAOTIC DIFFUSION MODELS This section aims to present the algorithmic implementation of mean-field score matching and training procedure with objective (P3). A.9.2. SAMPLING SCHEME FOR MEAN-FIELD CHAOS DIFFUSION MODELS To sample the denoising dynamics, this work proposes a modified Euler scheme, adapted for mean-field interacting particle systems (Bossy & Talay, 1997; dos Reis et al., 2022), and approximate the stochastic differential equations in the mean-field limit. The proposed scheme involves a four-step sampling procedure. |
| Open Source Code | No | No explicit statement or link regarding the release of the source code for the methodology described in this paper was found. |
| Open Datasets | Yes | Datasets. This paper utilizes Shape Net, a widely recognized dataset comprising a vast collection of 3D object models across multiple categories, and Med Shape Net, a curated collection of medical shape data designed for advanced imaging analysis. 1. Shape Net. (Chang et al., 2015) ... 2. Med Shape Net. (Li et al., 2023) |
| Dataset Splits | No | No explicit train/test/validation dataset splits with specific percentages or counts were found. |
| Hardware Specification | Yes | All experiments were conducted using a setup of 4 NVIDIA A100 GPUs. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., PyTorch 1.9, Python 3.8) were explicitly mentioned in the paper. |
| Experiment Setup | Yes | Table 4. Hyperparameters according to cardinality in data instances. Learning Rate 1.0e 3 1.0e 4 (VP SDE) σ2 t = βt, βt = βmin + t(βmax βmin), βmax = 20.0, βmin = 0.1 (Diffusion Steps) K {1, , 300}, |K| = 300 (Branching Ratio) b 2 (Branching Steps) K {100, 200} {50, 100, 150, 200} {50, 100, 150, 200, 250} (Initial Cardinality) {N0} 250 625 1250 3125 (Interaction Degree) k 10 3 3 3 |