Ultrahyperbolic Representation Learning
Authors: Marc Law, Jos Stam
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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. |