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 [1].
Roto-translated Local Coordinate Frames For Interacting Dynamical Systems
Authors: Miltiadis Kofinas, Naveen Nagaraja, Efstratios Gavves
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments in traffic scenes, 3D motion capture, and colliding particles demonstrate that the proposed approach comfortably outperforms the recent state-of-the-art. 5 Experiments We evaluate the proposed method, Lo CS, on 2D and 3D geometric graph dynamical systems from the literature. In 2D, we evaluate on a synthetic physics simulation dataset proposed by d NRI [19] and on traffic trajectory forecasting [4]. In 3D, we evaluate on an 3D-extended version of the charged particles [27] and on a motion capture dataset [10]. We compare with NRI [27], d NRI [19], and the very recent EGNN [41]. |
| Researcher Affiliation | Collaboration | Miltiadis Kofinas University of Amsterdam EMAIL Naveen Shankar Nagaraja BMW Group EMAIL Eftratios Gavves University of Amsterdam EMAIL |
| Pseudocode | No | The paper describes methods in text and equations, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code, data, and models will be available online1. 1https://github.com/mkofinas/locs |
| Open Datasets | Yes | On the synthetic 2D physics simulation we use the same experimental settings as in [19]. In the charged particles datasets the particles interact with one another via electrostatic forces. We extend the dataset by [27] from 2 to 3 dimensions... The in D dataset [4] is a real-world 2D traffic trajectory forecasting dataset... Last, we experiment with the CMU motion capture database [10] with 3D data... |
| Dataset Splits | Yes | We generate 30,000 scenes for training, 5,000 for validation and 5,000 for testing. The dataset contains 36 recordings; we split them in 19/7/10 for training, validation and testing. |
| Hardware Specification | No | The paper does not specify any particular hardware used for experiments (e.g., GPU/CPU models, memory details). |
| Software Dependencies | No | The paper mentions software components like MLPs, LSTMs, and Gumbel-Softmax, and refers to code of other methods, but does not provide specific version numbers for any software dependencies used for their own implementation. |
| Experiment Setup | No | The paper states 'The full implementations details are in appendix B.3.' but does not provide specific hyperparameter values or detailed training configurations within the main text. |