Learning in Implicit Generative Models
Authors: Shakir Mohamed, Balaji Lakshminarayanan
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | 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 fields, 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 {shakir,balajiln}@google.com |
| 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. |