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..
Globally Convergent Variational Inference
Authors: Declan McNamara, Jackson Loper, Jeffrey Regier
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In ablation studies and practical problems, we demonstrate that our results explain the behavior of NPE in non-asymptotic finite-neuron settings, and show that NPE outperforms ELBO-based optimization, which often converges to shallow local optima. |
| Researcher Affiliation | Academia | Declan Mc Namara Jackson Loper Jeffrey Regier Department of Statistics University of Michigan EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/declanmcnamara/gcvi_neurips. |
| Open Datasets | Yes | We use the MNIST dataset, freely available from torchvision under the BSD-3 License1 |
| Dataset Splits | No | The paper uses generated data for several experiments and mentions using N=1000 MNIST digits, but does not provide explicit training, validation, or test dataset splits (percentages or counts) or refer to standard splits with citations. |
| Hardware Specification | Yes | We used Py Torch (Paszke et al., 2019) for our experiments in accordance with its license, and NVIDIA Ge Force RTX 2080 Ti GPUs. |
| Software Dependencies | No | We used Py Torch (Paszke et al., 2019) for our experiments in accordance with its license... |
| Experiment Setup | Yes | SGD was performed using the Adam optimizer with a learning rate of ρ = 0.0001. We employ a learning rate scheduler that scales the learning rate as O(1/I), where I denotes the number of iterations. All models were fitted for 200,000 stochastic gradient steps... |