Riemannian Adaptive Optimization Methods
Authors: Gary Becigneul, Octavian-Eugen Ganea
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | 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. |