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..
Feedback Favors the Generalization of Neural ODEs
Authors: Jindou Jia, Zihan Yang, Meng Wang, Kexin Guo, Jianfei Yang, Xiang Yu, Lei Guo
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, extensive tests including trajectory prediction of a real irregular object and model predictive control of a quadrotor with various uncertainties, are implemented, indicating significant improvements over state-of-the-art model-based and learning-based methods. |
| Researcher Affiliation | Academia | 1Beihang University 2Hangzhou Innovation Institute of Beihang University 3Nanyang Technological University |
| Pseudocode | Yes | Algorithm 1 Learning neural feedback through domain randomization |
| Open Source Code | Yes | Codes are available at https://sites.google.com/view/feedbacknn. |
| Open Datasets | Yes | We test the effectiveness of the proposed method on an open-source dataset (Jia et al., 2024) |
| Dataset Splits | Yes | 21 trajectories are used for training, while 9 trajectories are used for testing. |
| Hardware Specification | Yes | It takes around 30 mins to run 50 epochs on a laptop with 13th Gen Intel(R) Core(TM) i9-13900H. ... As for the neural feedback form, due to the optimization problem being non-convex, a satisfactory result usually takes 10 mins to 1 hour of training time on a laptop with Intel(R) Core(TM) Ultra 9 185H 2.30 GHz. |
| Software Dependencies | No | The paper mentions optimizers like 'RMSprop optimizer' and 'Adam optimizer' but does not specify their version numbers or any other software dependencies with version information. |
| Experiment Setup | Yes | In training, we use RMSprop optimizer with the default learning rate of 0.001. The network is trained with a batch size of 20 for 400 iterations. ... In training, we use RMSprop optimizer with the learning rate of 0.01. The network is trained with a batch size of 100 for 2000 iterations. ... In training, we use Adam optimizer with the default learning rate of 0.001. The network is trained with a batch size of 20 for 1000 iterations. |