Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Smoothing the Geometry of Probabilistic Box Embeddings
Authors: Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, Andrew McCallum
ICLR 2019 | Venue PDF | 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 ο¬rst 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. |