Neural Diffeomorphic Non-uniform B-spline Flows
Authors: Seongmin Hong, Se Young Chun
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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. |