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..
Brain-like Variational Inference
Authors: Hadi Vafaii, Dekel Galor, Jacob L. Yates
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, i P-VAE outperforms both standard VAEs and Gaussian-based predictive coding models in sparsity, reconstruction, and biological plausibility, and scales to complex color image datasets such as Celeb A. i P-VAE also exhibits strong generalization to out-of-distribution inputs, exceeding hybrid iterative-amortized VAEs. |
| Researcher Affiliation | Academia | 1Redwood Center for Theoretical Neuroscience, UC Berkeley |
| Pseudocode | Yes | Algorithm 1 Iterative Generation Procedure for i P-VAE |
| Open Source Code | Yes | Code, data, and model checkpoints are available here: https://github.com/hadivafaii/Iterative VAE |
| Open Datasets | Yes | We train and evaluate models on two datasets: (1) whitened 16x16 natural image patches from the van Hateren dataset [100], used to assess convergence and reconstruction sparsity trade-offs; (2) MNIST [101], used for reconstruction, classification, and OOD generalization tests. ... In appendix C.9, we demonstrate practical utility by scaling i P-VAE to high-dimensional color images (Celeb A, CIFAR-10, tiny Image Net) |
| Dataset Splits | Yes | For our comparisons, we utilized the official implementations of sa-VAE [92], ia-VAE [91], and P-VAE [50]. Across models, we maintained consistent train/validation splits and hyperparameters unless otherwise specified. |
| Hardware Specification | Yes | training the models presented in Fig. 3 with Ttrain = 16 requires approximately three hours per model on an NVIDIA A6000 GPU. ... Our complete set of experiments, including all hyperparameter sweeps and model variants, was conducted over the course of approximately one week using six NVIDIA A6000 GPUs (48GB VRAM each). |
| Software Dependencies | Yes | We thank the developers of the software packages used in this project, including Py Torch [130], Num Py [131], Sci Py [132], scikit-learn [133], pandas [134], matplotlib [135], and seaborn [136]. |
| Experiment Setup | Yes | For the reconstruction-sparsity analysis (Fig. 4), we swept across Ttrain {8, 16, 32} and β values proportional to each Ttrain, with factors {0.5, 0.75, 1.0, 1.25, 1.5, 2.0, 3.0, 4.0}. ... For the van Hateren dataset, we used a learning rate of 0.002, a batch size of 200, and trained for 300 epochs. MNIST [221] models used identical hyperparameters but were trained for 400 epochs. |