Faithful Inversion of Generative Models for Effective Amortized Inference
Authors: Stefan Webb, Adam Golinski, Rob Zinkov, Siddharth N, Tom Rainforth, Yee Whye Teh, Frank Wood
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove the correctness of our approach and empirically show that the resulting minimally faithful inverses lead to better inference amortization than existing heuristic approaches. 3 Experiments We now consider the empirical impact of using Na MI compared with previous approaches. |
| Researcher Affiliation | Academia | Stefan Webb University of Oxford Adam Goli nski University of Oxford Robert Zinkov UBC N. Siddharth University of Oxford Tom Rainforth University of Oxford Yee Whye Teh University of Oxford Frank Wood UBC |
| Pseudocode | Yes | Algorithm 1 Na MI Graph Inversion |
| Open Source Code | Yes | Low-level details on the experimental setups can be found in Appendix D and an implementation at https://git.io/fx VQu. |
| Open Datasets | Yes | We learn a relaxed Bernoulli VAE with 30 latent variables on MNIST |
| Dataset Splits | Yes | Figures 6a and 6b show an estimate of KL(pθ(z|x)||qψ(z|x)) using the train and test sets respectively. |
| Hardware Specification | No | The paper does not specify any particular GPU models, CPU models, or other hardware used for the experiments. |
| Software Dependencies | No | While the paper mentions "MADE" or "neural networks," it does not specify software versions (e.g., PyTorch 1.x, TensorFlow 2.x, Python 3.x). |
| Experiment Setup | Yes | compared after 1000 epochs of learning the: (a) negative ELBO, and (b) negative AIS estimates, varying inference network factorizations and capacities (total number of parameters); Results are given in Figure 6 for depth d = 5 averaging over 10 runs. We hold the neural network capacities constant across methods and average over 10 runs. |