Smoothing the Geometry of Probabilistic Box Embeddings
Authors: Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, Andrew McCallum
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 EXPERIMENTS We perform experiments on the Word Net hypernym prediction task in order to evaluate the performance of these improvements in practice. |
| Researcher Affiliation | Academia | Xiang Li , Luke Vilnis , Dongxu Zhang, Michael Boratko & Andrew Mc Callum College of Information and Computer Sciences University of Massachusetts Amherst |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks clearly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Detailed hyperparameter settings and code to reproduce experiments can be found at https://github.com/Lorraine333/smoothed_box_embedding. |
| Open Datasets | Yes | We perform experiments on the Word Net hypernym prediction task... We apply our method to a market-basket task constructed using the Movie Lens dataset. Here, the task is to predict users preference for movie A given that they liked movie B. We first collect all pairs of user-movie ratings higher than 4 points (strong preference) from the Movie Lens-20M dataset. |
| Dataset Splits | Yes | We used the same train/dev/test split as in Vendrov et al. (2016). ... The training data contains 1,176 positive examples, and the dev and test sets contain 209 positive examples. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory specifications, or cloud instance types used for running its experiments. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with their version numbers, only generally stating that code is available to reproduce experiments. |
| Experiment Setup | No | The paper mentions that 'Detailed hyperparameter settings and code to reproduce experiments can be found at https://github.com/Lorraine333/smoothed_box_embedding', but it does not provide specific hyperparameter values or concrete system-level training configurations directly in the main text. |