Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Population Matching Discrepancy and Applications in Deep Learning
Authors: Jianfei Chen, Chongxuan LI, Yizhong Ru, Jun Zhu
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate that PMD overcomes the aforementioned drawbacks of MMD, and outperforms MMD on both tasks in terms of the performance as well as the convergence speed. |
| Researcher Affiliation | Academia | Jianfei Chen, Chongxuan Li, Yizhong Ru, Jun Zhu Dept. of Comp. Sci. & Tech., TNList Lab, State Key Lab for Intell. Tech. & Sys. Tsinghua University, Beijing, 100084, China EMAIL, EMAIL |
| Pseudocode | Yes | Figure 1: Pseudocode of PMD for parameter learning with graphical illustration of an iteration. |
| Open Source Code | No | The paper references a GitHub link for "Generative Moment Matching Networks" by Siddharth Agrawal [2], which is a third-party reference and not a link to the authors' own implementation code for the method described in this paper. |
| Open Datasets | Yes | We compare the performance of PMD and MMD on the standard Office [41] object recognition benchmark for domain adaptation. ... We compare PMD with MMD for image generation on the MNIST [28], SVHN [36] and LFW [20] dataset. |
| Dataset Splits | Yes | Following [8], we validate the domain regularization strength λ and the MMD kernel bandwidth σ on a random 100-sample labeled dataset on the target domain, but the model is trained without any labeled data from the target domain. |
| Hardware Specification | Yes | Our experiment is conducted on a machine with Nvidia Titan X (Pascal) GPU and Intel E5-2683v3 CPU. |
| Software Dependencies | Yes | We implement the models in Tensor Flow [1]. The CUDA program is compiled with nvcc 8.0 and the C++ program is compiled with g++ 4.8.4, while -O3 flag is used for both programs. |
| Experiment Setup | Yes | The classifier is a fully-connected neural network with a single hidden layer of 256 Re LU [15] units, trained with Ada Delta [51]. We apply batch normalization [21] on the hidden layer... We set the population size N = 2000 for both PMD and MMD, and the mini-batch size |B| = 100 for PMD. We use the Ada M optimizer [22] with batch normalization [21], and train the model for 100 epoches for PMD, and 500 epoches for MMD. |