Joint Generative Moment-Matching Network for Learning Structural Latent Code
Authors: Hongchang Gao, Heng Huang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | At last, extensive experiments on both synthetic and realworld datasets have verified the effectiveness and correctness of our proposed JGMMN. |
| Researcher Affiliation | Academia | Hongchang Gao, Heng Huang Department of Electrical and Computer Engineering, University of Pittsburgh, USA hongchanggao@gmail.com, heng.huang@pitt.edu |
| Pseudocode | No | The paper includes mathematical formulations and descriptions of processes but does not provide a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | We conduct experiments on five real-world datasets, which includes 3 image datasets: MNIST [Le Cun et al., 1998], USPS [Cai et al., 2011] , Extend Yale B (EYB), and 2 text datatsets: Reuters-10K [Xie et al., 2016], 20News1. |
| Dataset Splits | No | The paper mentions synthetic and real-world datasets and some sample counts, but it does not provide explicit training, validation, or test dataset splits (e.g., percentages or exact counts for each split). |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used to conduct the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions activation functions and network types but does not list specific software dependencies, such as programming languages, libraries, or frameworks with their version numbers. |
| Experiment Setup | Yes | Both the generation and inference network of the first synthetic dataset is a 3-layer MLP: [100, 100, 2]. Similarly, that of the second synthetic dataset is [100, 300, 2]. The activation function employed is Re LU [Nair and Hinton, 2010]. Note that the last layer employs the linear activation. The size of mini-batch is set as 500. The kernel employed in JMMD is the Gaussian kernel. Here, we use a mixture of several Gaussian kernels, that is k(xi, xj) = M m=1 km(xi, xj) where different kernels have different bandwidth parameters. In this paper, the bandwidth employed is {2.0, 5.0, 10.0, 20.0, 40.0, 80.0}. |