Results 31 to 40 of about 2,854,398 (286)

Symbolic regression in materials science [PDF]

MRS Communications, 2019
We showcase the potential of symbolic regression as an analytic method for use in materials research. First, we briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques.
Wang, Yiqun, Wagner, Nicholas, Rondinelli, James M.   +2 more
openaire   +2 more sources

Symbolic Expression Transformer: A Computer Vision Approach for Symbolic Regression [PDF]

, 2022
Symbolic Regression (SR) is a type of regression analysis to automatically find the mathematical expression that best fits the data. Currently, SR still basically relies on various searching strategies so that a sample-specific model is required to be ...
Li, Jiachen, Yuan, Ye, Shen, Hong-Bin
core   +1 more source

Symbolic Regression Algorithms with Built-in Linear Regression

CoRR, 2017
Recently, several algorithms for symbolic regression (SR) emerged which employ a form of multiple linear regression (LR) to produce generalized linear models. The use of LR allows the algorithms to create models with relatively small error right from the beginning of the search; such algorithms are thus claimed to be (sometimes by orders of magnitude ...
Jan Zegklitz, Petr Posík
openaire   +2 more sources

Symbolic regression extracted models.

, 2019
Symbolic regression extracted models.
Vsevolod Peysakhovich (6470357), Almoctar Hassoumi (6470354), Christophe Hurter (6470360)   +2 more
core   +1 more source

Multiview Symbolic Regression

Proceedings of the Genetic and Evolutionary Computation Conference
Published in GECCO-2024.
Etienne Russeil, Fabrício Olivetti de França, Konstantin L. Malanchev, Bogdan Burlacu, Emille E. O. Ishida, Marion Leroux, Clément Michelin, Guillaume Moinard, Emmanuel Gangler   +8 more
openaire   +4 more sources

Improving eye-tracking calibration accuracy using symbolic regression.

PLoS ONE, 2019
Eye tracking systems have recently experienced a diversity of novel calibration procedures, including smooth pursuit and vestibulo-ocular reflex based calibrations.
Almoctar Hassoumi, Vsevolod Peysakhovich, Christophe Hurter   +2 more
doaj   +1 more source

Nutrient/TOR signaling controls adipose mitochondrial transcription factor A (TFAM) to regulate organismal growth in Drosophila

FEBS Letters, EarlyView.
Animals must match their growth rate to available nutrients. We show that in Drosophila larvae, the nutrient‐sensing TOR kinase controls growth by regulating levels of TFAM, a key regulator of mitochondrial function, in the adipose tissue. When nutrients are abundant, high TOR activity suppresses TFAM, lowering mitochondrial bioenergetic activity and ...
Shrivani Sriskanthadevan‐Pirahas, Shahoon Khan, Abhishek Sharma, Byoungchun Lee, Savraj S. Grewal   +4 more
wiley   +1 more source

Symbolic regression settings.

, 2014
Symbolic regression settings.
Jan Broeckhove (553513), Philippe Beutels (43663), Sean Stijven (553511), Niel Hens (43661), Lander Willem (119855), Ekaterina Vladislavleva (553512)   +5 more
core   +1 more source

Globally Optimal Symbolic Regression

, 2017
International audienceIn this study we introduce a new technique for symbolic regression that guarantees global optimality. This is achieved by formulating a mixed integer non-linear program (MINLP) whose solution is a symbolic mathematical expression of
Dash, Sanjeeb, Austel, Vernon, Liberti, Leo, Horesh, Lior, Nannicini, Giacomo, Gunluk, Oktay, Schieber, Baruch   +6 more
core   +3 more sources

$\mathcal{CP}$-analyses with symbolic regression

SciPost Physics
Searching 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
doaj   +1 more source