Gaussian Copula Embeddings
Authors: Chien Lu, Jaakko Peltonen
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments on five different scenarios, the proposed model is shown to be effective, outperforming competitive methods in task-based evaluations and yielding insights in a social media analysis task. |
| Researcher Affiliation | Academia | Chien Lu Jaakko Peltonen Tampere University |
| Pseudocode | Yes | The complete stochastic inference procedure is given in Algorithm 1. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | The Anime rating data is a set of user ratings on anime movies and series collected from myanimelist.net. 1From Kaggle, https://www.kaggle.com/datasets/Cooper Union/anime-recommendations-database. The CIC Dark-net traffic data set [6]. Spanish Twitch gamers is a subgraph of the Twitch gamers graph data [17]. The Reddit Hyperlink Network [12]. |
| Dataset Splits | No | For all methods, when training the model, we hold out 10% of the data as the testing data set, and the trained models are used to predict the ratings in the test data. The paper specifies a test split but does not explicitly mention a separate validation split or how it was used if present. |
| Hardware Specification | No | The main text of the paper does not provide specific details on the hardware used for running experiments (e.g., GPU/CPU models, memory, or cloud instances). While the checklist indicates this information is in supplementary materials, it is not present in the main paper. |
| Software Dependencies | No | The paper mentions software components and algorithms like 'Adam optimizer' and 'Plackett-Luce model', but it does not provide specific version numbers for any libraries, frameworks, or programming languages used in the implementation. |
| Experiment Setup | Yes | The precision parameter λα is set to 0 corresponding to a very wide prior for α. In experiments we use M = 1000 mini-batches and 5 negative samples for each positive sample. The optimization then updates the embedding vectors in each epoch by gradient steps with step sizes chosen by the Adam optimizer. |