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..
3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations
Authors: Zhihua Wang, Stefano Rosa, Bo Yang, Sen Wang, Niki Trigoni, Andrew Markham
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show the viability and the generalisation properties of the proposed architecture. ... 4 Experiments ... 4.3 Results and Discussion |
| Researcher Affiliation | Academia | Zhihua Wang 1, Stefano Rosa 1, Bo Yang1, Sen Wang2, Niki Trigoni1 and Andrew Markham1 1Department of Computer Science, University of Oxford 2School of Engineering and Physical Sciences, Heriot-Watt University {name.surname}@cs.ox.ac.uk, EMAIL |
| Pseudocode | No | The paper describes the network architecture and implementation details (Table 1) but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper states, 'The full generated datasets will be released for the sake of reproducibility and testing.', but it does not mention releasing the source code for the methodology itself. |
| Open Datasets | Yes | The full generated datasets will be released for the sake of reproducibility and testing. |
| Dataset Splits | No | The paper mentions 'training data' and 'test on unseen values' but does not specify exact training/validation/test dataset splits by percentages or counts. |
| Hardware Specification | Yes | The network was implemented with Tensorflow 1.4 and trained on a single Nvidia Pascal Titan X GPU. |
| Software Dependencies | Yes | The network was implemented with Tensorflow 1.4 |
| Experiment Setup | Yes | The network was trained with a batch size of 8 using the Adam optimizer, with lr = 5e 5, β1 = 0.5, β2 = 0.999,ϵ = 1e 8. λ is set to 10 in Eq. 8. |