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..
Matrix Manifold Neural Networks++
Authors: Xuan Son Nguyen, Shuo Yang, Aymeric Histace
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of the proposed approach in the human action recognition and node classification tasks. |
| Researcher Affiliation | Academia | Xuan Son Nguyen, Shuo Yang, Aymeric Histace ETIS, UMR 8051, CY Cergy Paris University, ENSEA, CNRS, France EMAIL |
| Pseudocode | Yes | Algorithm 1: Computation of Pseudo-gyrodistances |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a direct link to a code repository for its methodology. |
| Open Datasets | Yes | We use three datasets, i.e., HDM05 (M uller et al., 2007), FPHA (Garcia-Hernando et al., 2018), and NTU RBG+D 60 (NTU60) (Shahroudy et al., 2016). |
| Dataset Splits | Yes | We use the 70/15/15 percent splits (Chami et al., 2019) for Airport dataset, and standard splits in GCN Kipf & Welling (2017) for Pubmed and Cora datasets. |
| Hardware Specification | Yes | Experiments are conducted on a machine with Intel Core i7-8565U CPU 1.80 GHz 24GB RAM. |
| Software Dependencies | No | The paper mentions using "Py Torch framework" but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | These networks are trained using cross-entropy loss and Adadelta optimizer for 2000 epochs. The learning rate is set to 10 3. The factors β (see Proposition 3.4) and λ (see Definition 3.9) are set to 0 and 1, respectively. ... We use a batch size of 32 for HDM05 and FPHA datasets, and a batch size of 256 for NTU60 dataset. |