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..
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Authors: Dustin Tran, Rajesh Ranganath, David Blei
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate diverse applications: a large-scale physical simulator for predator-prey populations in ecology; a Bayesian generative adversarial network for discrete data; and a deep implicit model for text generation. |
| Researcher Affiliation | Academia | Dustin Tran Columbia University Rajesh Ranganath Princeton University David M. Blei Columbia University |
| Pseudocode | Yes | Algorithm 1: Likelihood-free variational inference (LFVI) |
| Open Source Code | No | The paper states that the algorithm is available in Edward [53], which is a probabilistic programming language, but does not provide specific open-source code for the methodology implemented in this paper. |
| Open Datasets | Yes | Table 1: Classification accuracy of Bayesian GAN and Bayesian neural networks across small to medium-size data sets. Crabs Pima Covertype MNIST. |
| Dataset Splits | No | The paper mentions using datasets like MNIST and Lotka-Volterra simulations but does not specify the train/validation/test splits or a methodology for them. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models or types) used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of Edward, a probabilistic programming language, but does not specify its version number or any other software dependencies with specific versions. |
| Experiment Setup | Yes | We initialize parameters from a standard normal and apply gradient descent with ADAM. g( | θ) is a 2-layer multilayer perception with Re LU activations, batch normalization, and is parameterized by weights and biases θ. We place normal priors, θ N(0, 1). |