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..
Neural Diffeomorphic Non-uniform B-spline Flows
Authors: Seongmin Hong, Se Young Chun
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we performed experiments on solving the force matching problem in Boltzmann generators, demonstrating that our C2-diffeomorphic non-uniform B-spline flows yielded solutions better than previous spline flows and faster than smooth normalizing flows. |
| Researcher Affiliation | Academia | Seongmin Hong1, Se Young Chun1,2* 1 Department of Electrical and Computer Engineering, Seoul National University, Republic of Korea 2 INMC, Interdisciplinary Program in AI, Seoul National University, Republic of Korea |
| Pseudocode | Yes | Algorithm 1: Non-uniform B-spline parameter generation |
| Open Source Code | Yes | Our source code is publicly available at https://github.com/smhongok/Non-uniform-B-spline-Flow. |
| Open Datasets | No | The paper mentions using "alanine dipeptide" for experiments but does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for its public availability. |
| Dataset Splits | No | The paper mentions training and testing but does not provide specific details on dataset splits (e.g., percentages or sample counts for train, validation, and test sets) within the main text. |
| Hardware Specification | Yes | All computations were conducted on NVIDIA Ge Force RTX3090. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Two loss functions were used: NLL loss LNLL or NLL + FM loss (1 λFM)LNLL + λFMLFM where f(x(n)) x log px(x(n)) 2 Ex p x h f(x) x log px(x) 2 2 i , and we set λFM = 0.001. |