Spread Divergence
Authors: Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show results for a toy experiment in figure(4), learning the mixing matrix A in a deterministic non-square setting. MNIST Experiment: We trained a δ-VAE on MNIST (Le Cun et al., 2010)... Celeb A Experiment: We trained a δ-VAE on the Celeb A dataset (Liu et al., 2015)... Table 1. Celeb A FID Scores. |
| Researcher Affiliation | Academia | Mingtian Zhang 1 Peter Hayes 1 Tom Bird 1 Raza Habib 1 David Barber 1 1Department of Computer Science, University College London, UK. Correspondence to: Mingtian Zhang <mingtian.zhang.17@ucl.ac.uk>. |
| Pseudocode | No | The paper describes its methods using prose and mathematical equations but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions a third-party tool's GitHub link ('github.com/bioinf-jku/TTUR') but does not provide any concrete access to the source code for the methodology described in this paper. |
| Open Datasets | Yes | MNIST Experiment: We trained a δ-VAE on MNIST (Le Cun et al., 2010)... and Celeb A Experiment: We trained a δ-VAE on the Celeb A dataset (Liu et al., 2015)... |
| Dataset Splits | No | The paper mentions using 'training data' for experiments on MNIST and Celeb A datasets but does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We first train with one epoch a standard VAE as initialization to all models and keep the latent code z ∼ N (z 0, IZ) fixed when sampling from these models thereafter, so we can more easily compare the sample quality. gθ( ) is a neural network that contains 3 feed forward layers. (MNIST) gθ( ) is a neural network contains 4 convolution layers. (Celeb A) We use Sy=1, Sz=1000 samples and 2000 EM iterations to estimate A. (ICA). |