Results 51 to 60 of about 13,019 (261)

Class Symbolic Regression: Gotta Fit ’Em All

The Astrophysical Journal Letters
We introduce “Class Symbolic Regression” (Class SR), the first framework for automatically finding a single analytical functional form that accurately fits multiple data sets—each realization being governed by its own (possibly) unique set of fitting ...
Wassim Tenachi, Rodrigo Ibata, Thibaut L. François, Foivos I. Diakogiannis   +3 more
doaj   +1 more source

End-To-End Symbolic Regression with Transformers

Advances in Neural Information Processing Systems 35, 2022
Symbolic regression, the task of predicting the mathematical expression of a function from the observation of its values, is a difficult task which usually involves a two-step procedure: predicting the "skeleton" of the expression up to the choice of numerical constants, then fitting the constants by optimizing a non-convex loss function.
Kamienny, Pierre-Alexandre, d'Ascoli, Stéphane, Lample, Guillaume, Charton, François   +3 more
openaire   +4 more sources

CSF Monoamine Metabolites and Cognitive Trajectory in Early Parkinson's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Imaging and postmortem studies indicate that abnormalities in monoaminergic neurotransmission contribute to cognitive impairment in Parkinson's disease (PD). However, it remains uncertain if cerebrospinal fluid (CSF) monoamine metabolites can serve as biomarkers of cognitive decline in early PD.
Jing‐Yu Shao, Rong Li, Yu‐Xiang Jiao, Kai Ma, Hong‐Qi Yang, Zheng‐Yu Sun, Jie‐Wen Zhang, Shuai Chen   +7 more
wiley   +1 more source

SPG4 and Dementia: Expanding the Clinical Spectrum

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Hereditary spastic paraplegia (HSP) is a group of disorders characterized by progressive spasticity and lower limb weakness, with mutations in SPG4/SPAST being the most common cause. Detailed studies and clinical and molecular comparisons across different populations are missing.
Emanuele Panza, Arun Meyyazhagan, Eliseo Picchi, Gustavo Ribas, Ingrid Faber, Ryosuke Miyamoto, Preethi Basavaraju, Paolo Eusebi, Haripriya Kuchi Bhotla, Mario Stasi, Fabrizio Gaudiello, Francesco Patti, Filippo Maria Santorelli, Marcondes Cavalcante França Jr, José Luiz Pedroso, Orlando Graziani Povoas Barsottini, Hélio Afonso Ghizoni Teive, Peter Henry St George‐Hyslop, Toshitaka Kawarai, Antonio Orlacchio   +19 more
wiley   +1 more source

salmon: A Symbolic Linear Regression Package for Python

Journal of Statistical Software
One of the most attractive features of R is its linear modeling capabilities. We describe a Python package, salmon, that brings the best of R's linear modeling functionality to Python in a Pythonic way - by providing composable objects for specifying ...
Alex Boyd, Dennis L. Sun
doaj   +1 more source

Symbolic regression metamodel based multi-response optimization of EDM process [PDF]

FME Transactions, 2020
Electrical Discharge Machining (EDM) is a popular non-traditional machining process that is widely used due to its ability to machine hard and brittle materials. It does not require a cutting tool and can machine complex geometries easily.
Ghadai Ranjan Kumar, Kalita Kanak, Gao Xiao-Zhi   +2 more
doaj  

Advancing Age Modulates Associations Between Cognitive Impairment and Brain Volumes in Early MS

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Cognitive impairment is common in multiple sclerosis (MS), but manifestations following the first demyelinating event are relatively unexplored. We investigated cross‐sectional associations between magnetic resonance imaging (MRI)–derived brain volumes and the presence of cognitive impairment outcomes five years after the first ...
Piriyankan Ananthavarathan, Marco Pitteri, Michael Foster, Sara Collorone, Sara Salama, Elisa Colato, Ferran Prados, Baris Kanber, Marios Yiannakas, Claudia A. M. Gandini Wheeler‐Kingshott, Indran Davagnanam, Frederik Barkhof, Declan Chard, Olga Ciccarelli, Ahmed Toosy   +14 more
wiley   +1 more source

Symbolic regression for defect interactions in 2D materials

Materials & Design
Machine learning models have become firmly established across all scientific fields. Extracting features from data and making inferences based on them with neural network models often yields high accuracy; however, this approach has several drawbacks ...
Mikhail Lazarev, Andrey Ustyuzhanin
doaj   +1 more source

Symbolic Regression on Network Properties

, 2017
Networks are continuously growing in complexity, which creates challenges for determining their most important characteristics. While analytical bounds are often too conservative, the computational effort of algorithmic approaches does not scale well with network size.
Marcus Märtens, Fernando A. Kuipers, Piet Van Mieghem   +2 more
openaire   +3 more sources

Memory and Resting‐State Connectivity in Acute Transient Global Amnesia: A Case–Control fMRI Study

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network‐level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their ...
Elias El Otmani, Emmanuel Barbeau, Béatrice Lemesle, Emilie Milongo‐Rigal, Sophie Fernandez, Sandrine Charpentier, Jean‐François Albucher, Nicolas Raposo, Fabrice Bonneville, Patrice Péran, Jérémie Pariente   +10 more
wiley   +1 more source