A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm
Authors: Gersende Fort, Eric Moulines, Hoi-To Wai
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results support our findings. |
| Researcher Affiliation | Academia | Gersende Fort Institut de Math ematiques de Toulouse Universit e de Toulouse; CNRS UPS, Toulouse, France gersende.fort@math.univ-toulouse.fr Eric Moulines Centre de Math ematiques Appliqu ees Ecole Polytechnique, France CS Dpt, HSE University, Russian Federation eric.moulines@polytechnique.edu Hoi-To Wai Department of SEEM The Chinese University of Hong Kong Shatin, Hong Kong htwai@cuhk.edu.hk |
| Pseudocode | Yes | Algorithm 1: The SPIDER-EM algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability. |
| Open Datasets | Yes | MNIST Dataset. We perform experiment on the MNIST dataset to illustrate the effectiveness of SPIDER-EM on real data; this example is taken from [23, Section 5]. |
| Dataset Splits | No | The paper uses the MNIST dataset but does not specify training, validation, or test splits. It mentions preprocessing steps and estimating a GMM model. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For SPIDER-EM, we set b = dpn/20e, kin = dn/be and a fixed step size γk = 0.01. The minibatch size is set to be b = 100 and the stepsize γ = 5 10 3 except for i EM where γ = 1. The same initial value b Sinit is used for all experiments. We have implemented the procedure of [19] in order to obtain the initialization init and then we set b Sinit def = s( init) ( W(b Sinit) = 58.3). |