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..
f-Divergence Variational Inference
Authors: Neng Wan, Dapeng Li, NAIRA HOVAKIMYAN
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical examples, including variational autoencoders and Bayesian neural networks, are provided to demonstrate the effectiveness and the wide applicability of f-VI. |
| Researcher Affiliation | Collaboration | Neng Wan1 EMAIL Dapeng Li2 EMAIL Naira Hovakimyan1 EMAIL 1 University of Illinois at Urbana-Champaign, Urbana, IL 61801 2 Anker Innovations, Shenzhen, China |
| Pseudocode | No | A reference black-box f-VI algorithm and the optimization schemes for a few concrete divergences are given in the SM. ... A reference mean-field VI algorithm along with a concrete realization example under KL divergence is provided in the SM. |
| Open Source Code | No | The paper does not provide a direct link to the source code for the methodology or explicitly state that the code is publicly released in the main text. |
| Open Datasets | Yes | The linear regression is performed with twelve datasets from the UCI Machine Learning Repository [36]. ... Bayesian VAE for image reconstruction and generation on the datasets of Caltech 101 Silhouettes [37], Frey Face [38], MNIST [39], and Omniglot [40]. |
| Dataset Splits | Yes | Each dataset is randomly split into 90%/10% for training and testing |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | Adam optimizer with recommended parameters in [35] is employed for stochastic optimization, if not specified. (Note: No version number for Adam is provided.) |
| Experiment Setup | Yes | Adam optimizer with recommended parameters in [35] is employed for stochastic optimization, if not specified. ... The IW-reparameterization gradient (14) with L = 3 and K = 1000 is adopted for the training on a dataset of 500 observations... The IW-reparameterization gradient with L = 5, K = 50 and mini-batch size of 32 is employed for training. After 20 trials with 500 training epochs in each trial... The reparameterization gradient with K = 3, L = 1 is used for training. After 20 trials with 200 training epochs in each trial... |