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..
Ultrahyperbolic Representation Learning
Authors: Marc Law, Jos Stam
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our novel framework is applied to graph representations. We now experimentally validate our proposed optimization methods and the effectiveness of our dissimilarity function. |
| Researcher Affiliation | Industry | This article was entirely funded by NVIDIA corporation. Marc Law and Jos Stam completed this working from home during the COVID-19 pandemic. |
| Pseudocode | Yes | Algorithm 1 Pseudo-Riemannian optimization on Qp,q β |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the methodology's source code. |
| Open Datasets | Yes | Zachary s karate club dataset [30]. Due to lack of space, we also report in the supp. material similar experiments on a larger hierarchical dataset [9] that describes co-authorship from papers published at NIPS from 1988 to 2003. |
| Dataset Splits | No | The paper does not specify exact percentages or sample counts for training, validation, or test splits, nor does it refer to predefined splits from cited works. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | We coded our approach in Py Torch [22] that automatically calculates the Euclidean gradient f(xi). While PyTorch is mentioned, a specific version number is not provided. |
| Experiment Setup | Yes | Initially, a random set of vectors {zi}n i=1 is generated close to the positive pole ( p |β|, 0, , 0) Qp,q β with every coordinate perturbed uniformly with a random value in the interval [ ε, ε] where ε > 0 is chosen small enough so that zi 2 q < 0. We set β = 1, ε = 0.1 and τ = 10 2. Initial embeddings are generated as follows: i, xi = p | zi 2 q| Qp,q β . In each test, we vary the number of time dimensions q + 1 while the ambient space is of fixed dimensionality d = p + q + 1 = 10. |