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..
Flexible and accurate inference and learning for deep generative models
Authors: Eszter Vértes, Maneesh Sahani
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that the new algorithm outperforms current state-of-the-art methods on synthetic, natural image patch and the MNIST data sets. |
| Researcher Affiliation | Academia | Eszter Vértes Maneesh Sahani Gatsby Computational Neuroscience Unit University College London London, W1T 4JG EMAIL |
| Pseudocode | Yes | Algorithm 1 DDC Helmholtz Machine training Initialise θ repeat Sleep phase: for s = 1 . . . S, sample: z(s) L , ..., z(s) 1 , x(s) pθ(x, z1, ..., z L) update recognition parameters {φl} [eq. 7] update function approximators {αl, βl} [appendix] Wake phase: x {minibatch} evaluate rl(x, φ) [eq. 8] update θ: θ [ θF(x, r(x, φ), θ) [appendix] until |[ θF| < threshold |
| Open Source Code | No | The paper does not provide any specific links to open-source code or an explicit statement about its public availability. |
| Open Datasets | Yes | We tested the scalability of the DDC-HM by applying it to a natural image data set [22]. We used the binarised MNIST dataset of 28x28 images of handwritten digits [23]. |
| Dataset Splits | No | The paper mentions using synthetic data for training and a test set for MNIST (N=10000), but it does not specify the explicit training, validation, and test dataset splits (e.g., percentages, counts, or detailed methodology for partitioning) that would allow reproduction of the data partitioning. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions the "Adam optimiser" [19] but does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their specific versions). |
| Experiment Setup | Yes | We used a recognition model with a hidden layer of size 100, and K1 = K2 = 100 encoding functions for each latent layer, with 200 sleep samples, and learned the parameters of the conditional distributions p(x|z1) and p(z1|z2) while keeping the prior on z2 fixed (m=3, σ=0.1). We initialised each model to the true generative parameters and ran the algorithms until convergence (1000 epochs, learning rate: 10−4, using the Adam optimiser; [19]). |