Improving Local Identifiability in Probabilistic Box Embeddings
Authors: Shib Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our model to a variety of synthetic problems and demonstrate that the new objective function correctly solves cases that are not solvable by any previous approaches to ļ¬tting box models. In real-world data, we demonstrate improved performance on a Word Net completion task and a Movie Lens density estimation task. |
| Researcher Affiliation | Academia | Shib Sankar Dasgupta Department of Computer Science University of Massachusetts, Amherst ssdasgupta@cs.umass.edu |
| Pseudocode | Yes | For ease of understanding, we provide a concrete instantiation of our algorithm for learning from pairwise conditional probabilities, incorporating all approximations, in Appendix D. |
| Open Source Code | Yes | Source code and data for the experiments are available at https://github.com/iesl/ gumbel-box-embeddings. |
| Open Datasets | Yes | Most of the recent geometric embeddings methods [26, 13, 5, 10, 18] use Word Net [14] to demonstrate the capability of their model to naturally represent real-world hierarchical data. |
| Dataset Splits | No | The paper discusses training and testing, but does not explicitly provide details about a separate validation split (e.g., percentages, sample counts, or cross-validation scheme) in the main text. |
| Hardware Specification | No | The paper does not specify the hardware used for running experiments (e.g., specific GPU or CPU models, memory details). |
| Software Dependencies | No | We use Weights & Biases package [2] to manage our experiments. The paper cites the software but does not specify its version number or other software dependencies with versions. |
| Experiment Setup | No | We train the embeddings via gradient descent, using KL-divergence loss. The paper mentions this general training approach and refers to appendices for 'full training details', but concrete hyperparameters are not detailed in the main text. |