Amortized Population Gibbs Samplers with Neural Sufficient Statistics
Authors: Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem Van De Meent
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate APG samplers on three different tasks. |
| Researcher Affiliation | Academia | 1Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA 2Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA. |
| Pseudocode | Yes | Algorithm 1 Sequential Monte Carlo sampler... Algorithm 2 Amortized Population Gibbs Sampler |
| Open Source Code | No | The paper does not provide an explicit statement or link to publicly available source code for the methodology described. |
| Open Datasets | Yes | In the bouncing MNIST model, our data is a corpus of video frames that contain multiple moving MNIST digits. |
| Dataset Splits | No | The paper specifies training and testing corpora, but it does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper mentions 'GPU memory' but does not provide specific details on the hardware used, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific Python libraries or frameworks). |
| Experiment Setup | Yes | We train with K = 5 sweeps, L = 10 particles, 20 instances per batch, learning rate 2.5 10 4, and 2 105 gradient steps. ... We train our model with K = 8 sweeps, L = 10 particles, 20 instances per batch, learning rate 10 4, and 3 105 gradient steps. ... with K = 5 sweeps, L = 10 particles, 5 instances per batch, learning rate 10 4, and 1.2 107 gradient steps. |