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..
Learning in Implicit Generative Models
Authors: Shakir Mohamed, Balaji Lakshminarayanan
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Here, we develop our understanding of GANs with the aim of forming a rich view of this growing area of machine learning to build connections to the diverse set of statistical thinking on this topic, of which much can be gained by a mutual exchange of ideas. We frame GANs within the wider landscape of algorithms for learning in implicit generative models models that only specify a stochastic procedure with which to generate data and relate these ideas to modelling problems in related ๏ฌelds, such as econometrics and approximate Bayesian computation. We develop likelihood-free inference methods and highlight hypothesis testing as a principle for learning in implicit generative models, using which we are able to derive the objective function used by GANs, and many other related objectives. |
| Researcher Affiliation | Industry | Shakir Mohamed and Balaji Lakshminarayanan Deep Mind, London EMAIL |
| Pseudocode | No | The paper describes various statistical and mathematical formulations, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the methodology described is publicly available. |
| Open Datasets | No | The paper is a theoretical discussion of implicit generative models and does not describe specific experiments or use any particular datasets that would be made publicly available. |
| Dataset Splits | No | The paper is theoretical and does not detail experimental procedures, thus it does not provide information on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies or their version numbers, as it does not detail any implementation or experimental setup. |
| Experiment Setup | No | The paper is theoretical and focuses on conceptual and mathematical developments, without providing details on specific experimental setups or hyperparameters. |