Student-t Variational Autoencoder for Robust Density Estimation
Authors: Hiroshi Takahashi, Tomoharu Iwata, Yuki Yamanaka, Masanori Yamada, Satoshi Yagi
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments with various datasets show that training of the Student-t VAE is robust, and the Student-t VAE achieves high density estimation performance. |
| Researcher Affiliation | Industry | 1 NTT Software Innovation Center 2 NTT Communication Science Laboratories 3 NTT Secure Platform Laboratories |
| Pseudocode | No | The paper describes the methods mathematically but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide a statement or link indicating that the source code for the proposed methodology is openly available. |
| Open Datasets | Yes | The SMTP data is the same as the data used in Section 3, where we used 10% of this dataset for training. We also used 10% of this dataset for validation and the remaining 80% for test. The Aloi data is the Amsterdam library of object images [Geusebroek et al., 2005], and the Thyroid, Cancer and Satellite datasets were obtained from the UCI Machine Learning Repository [Lichman, 2013]. We used the transformations of these datasets by [Goldstein and Uchida, 2016] 3. We used 50% of the dataset for training, 10% for validation, and the remaining 40% for test. The total number of data points and the dimensions of the observation of the five datasets are listed in Table 1. |
| Dataset Splits | Yes | We used 10% of this dataset for validation and the remaining 80% for test. ... We used 50% of the dataset for training, 10% for validation, and the remaining 40% for test. |
| Hardware Specification | Yes | We used the following setup: CPU was Intel Xeon E5-2640 v4 2.40GHz, the memory size was 1 TB, and GPU was NVIDIA Tesla M40. |
| Software Dependencies | No | The paper mentions 'scikit-learn' and 'Adam' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We used two-layer neural networks (500 hidden units per layer) as the encoder and the decoder, and a hyperbolic tangent as the activation function. We trained the VAE by using Adam [Kingma and Ba, 2014] with mini-batch size of 100. We set the sample size of the reparameterization trick to L = 1. The maximum number of epochs was 500, and we used early-stopping [Goodfellow et al., 2016] based on the validation data. We used a two-dimensional latent variable vector z with the SMTP data, and a 20-dimensional latent variable vector for the other datasets. |