Results 31 to 40 of about 12,301 (257)
The Application of Symbolic Regression on Identifying Implied Volatility Surface
Mathematics, 2023 One important parameter in the Black–Scholes option pricing model is the implied volatility. Implied volatility surface (IVS) is an important concept in finance that describes the variation of implied volatility across option strike price and time to ...
Jiayi Luo, Cindy Long Yu
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Smooth Symbolic Regression: Transformation of Symbolic Regression into a Real-Valued Optimization Problem [PDF]
, 2015 The typical methods for symbolic regression produce rather abrupt changes in solution candidates. In this work, we have tried to transform symbolic regression from an optimization problem, with a landscape that is so rugged that typical analysis methods do not produce meaningful results, to one that can be compared to typical and very smooth real ...
Pitzer, Erik, Kronberger, Gabriel
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Priors for symbolic regression
Proceedings of the Companion Conference on Genetic and Evolutionary Computation, 2023 8+2 pages, 2 figures.
Bartlett, D, Desmond, H, Ferreira, P
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Effective therapeutic targeting of CTNNB1‐mutant hepatoblastoma with WNTinib
Molecular Oncology, EarlyView.WNTinib, a Wnt/CTNNB1 inhibitor, was tested in hepatoblastoma (HB) experimental models. It delayed tumor growth and improved survival in CTNNB1‐mutant in vivo models. In organoids, WNTinib outperformed cisplatin and showed enhanced efficacy in combination therapy, supporting its potential as a targeted treatment for CTNNB1‐mutated HB.
Ugne Balaseviciute +17 more
wiley +1 more source
$\mathcal{CP}$-analyses with symbolic regression
SciPost PhysicsSearching for $\mathcal{CP}$ violation in Higgs interactions at the LHC is as challenging as it is important. Although modern machine learning outperforms traditional methods, its results are difficult to control and interpret, which is especially ...
Henning Bahl, Elina Fuchs, Marco Menen, Tilman Plehn
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Symbolic Regression on FPGAs for Fast Machine Learning Inference [PDF]
EPJ Web of ConferencesThe high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints.
Tsoi Ho Fung +9 more
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Universal Approach to Solution of Optimization Problems by Symbolic Regression
Applied Sciences, 2021 Optimization problems and their solution by symbolic regression methods are considered. The search is performed on non-Euclidean space. In such spaces it is impossible to determine a distance between two potential solutions and, therefore, algorithms ...
Elena Sofronova, Askhat Diveev
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, 2019 Interpretability is crucial for machine learning in many scenarios such as quantitative finance, banking, healthcare, etc. Symbolic regression (SR) is a classic interpretable machine learning method by bridging X and Y using mathematical expressions composed of some basic functions.
Jin, Ying +4 more
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LDAcoop: Integrating non‐linear population dynamics into the analysis of clonogenic growth in vitro
Molecular Oncology, EarlyView.Limiting dilution assays (LDAs) quantify clonogenic growth by seeding serial dilutions of cells and scoring wells for colony formation. The fraction of negative wells is plotted against cells seeded and analyzed using the non‐linear modeling of LDAcoop.
Nikko Brix +13 more
wiley +1 more source
Modeling of Tunneling Total Loads Based on Symbolic Regression Algorithm
Applied Sciences, 2021 The tunneling total load is one of the core control parameters for safe and efficient construction using tunneling machines. However, because the tunneling process involves complex coupling relationships between the equipment and the local geology ...
Liting Zhang +3 more
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