RMLR: Extending Multinomial Logistic Regression into General Geometries
Authors: Ziheng Chen, Yue Song, Rui Wang, Xiaojun Wu, Nicu Sebe
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on different Riemannian backbone networks validate the effectiveness of our framework. |
| Researcher Affiliation | Academia | Ziheng Chen1, Yue Song2 , Rui Wang3, Xiao-Jun Wu3, Nicu Sebe1 1 University of Trento, 2 Caltech, 3 Jiangnan University |
| Pseudocode | No | The paper describes methods through mathematical equations and textual explanations, but does not include a distinct section or figure explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | The code is available at https://github.com/Git ZH-Chen/RMLR. |
| Open Datasets | Yes | Radar2: This dataset [10] consists of 3,000 synthetic radar signals. ... HDM053: This dataset [44] contains 2,273 skeleton-based motion capture sequences... Hinss20214: This dataset [28] is a recent competition dataset... Disease [2]: It represents a disease propagation tree... Cora [54]: It is a citation network... Pubmed [46]: This is a standard benchmark... G3D[6]: This dataset consists of 663 sequences... |
| Dataset Splits | Yes | On the Hinss2021 dataset, models are fit and evaluated with a randomized leave 5% of the sessions (inter-session) or subjects (inter-subject) out cross-validation (CV) scheme. On other datasets, K-fold experiments are carried out with randomized initialization and split. |
| Hardware Specification | Yes | All experiments use an Intel Core i9-7960X CPU with 32GB RAM and an NVIDIA GeForce RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions software like PyTorch and Pytorch3D, and optimizers like Riemannian AMSGrad, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | On the Radar and HDM05 datasets, the learning rate is 1e-2, and the batch size is 30. On the Hinss2021 dataset, following [37], the learning rate is 1e-3 with a 1e-4 weight decay, and batch size is 50. The maximum training epoch is 200, 200, and 50, respectively. We use the standard-cross entropy loss as the training objective and optimize the parameters with the Riemannian AMSGrad optimizer [4]. |