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..
Riemannian Adaptive Optimization Methods
Authors: Gary Becigneul, Octavian-Eugen Ganea
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we show faster convergence and to a lower train loss value for Riemannian adaptive methods over their corresponding baselines on the realistic task of embedding the Word Net taxonomy in the Poincar e ball. |
| Researcher Affiliation | Academia | Gary B ecigneul, Octavian-Eugen Ganea Department of Computer Science ETH Z urich, Switzerland |
| Pseudocode | Yes | Figure 1: Comparison of the Riemannian and Euclidean versions of AMSGRAD. (a) RAMSGRAD in M1 Mn. (b) AMSGRAD in Rn. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability. |
| Open Datasets | Yes | For this, we follow (Nickel & Kiela, 2017) and embed the transitive closure of the Word Net noun hierarchy (Miller et al., 1990) in the n-dimensional Poincar e model Dn of hyperbolic geometry |
| Dataset Splits | Yes | For link prediction we sample a validation set of 2% edges from the set of transitive closure edges that contain no leaf node or root. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For all methods we use the same burn-in phase described in (Nickel & Kiela, 2017) for 20 epochs, with a fixed learning rate of 0.03 and using RSGD with retraction as explained in Sec. 2.2. ...We always use β1 = 0.9 and β2 = 0.999 for these methods as these achieved the lowest training loss. |