Correlated Variational Auto-Encoders
Authors: Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on matching and link prediction on public benchmark rating datasets and spectral clustering on a synthetic dataset show the effectiveness of the proposed method over baseline algorithms. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Columbia University, New York, New York, USA 2Netflix Inc., Los Gatos, California, USA 3Erik Jonsson School of Engineering & Computer Science, University of Texas at Dallas, Richardson, Texas, USA. |
| Pseudocode | Yes | Algorithm 1 Computing all weights w MAS G,e |
| Open Source Code | Yes | Code is available at https://github.com/datang1992/CorrelatedVAEs. |
| Open Datasets | Yes | We use the Movie Lens 20M dataset (Harper & Konstan, 2016). (...) In this experiment, we use the Epinions dataset (Massa & Avesani, 2007) |
| Dataset Splits | Yes | For all the experiments, we did a stochastic train/test split over users with a 90/10 ratio. (...) To split the train/test dataset for the link prediction task, for each vertex vi V , we hold out max 1, 1 20 degree(vi) edges on vi as the testing edge set Etest, and put all edges that are not selected into the training edge set Etrain. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. It does not mention any specific hardware used for training or evaluation. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4). |
| Experiment Setup | Yes | For CVAEcorr, we set the negative sampling regularization parameter γ = 1. (...) Here we apply negative sampling regularization parameter γ = 0.1 for CVAEcorr. (...) Here we apply a large regularization value of γ = 1000 for the CVAEcorr |