Filters








75 Hits in 5.2 sec

Nearly Optimal Pricing Algorithms for Production Constrained and Laminar Bayesian Selection [article]

Nima Anari, Rad Niazadeh, Amin Saberi, Ali Shameli
2018 arXiv   pre-print
We study online pricing algorithms for the Bayesian selection problem with production constraints and its generalization to the laminar matroid Bayesian online selection problem.  ...  We give the first Polynomial-Time Approximation Scheme (PTAS) for the above problem as well as its generalization to the laminar matroid Bayesian online selection problem when the depth of the laminar  ...  Production Constrained Bayesian Selection The goal of this section is to first formalize the production constrained Bayesian selection problem, and then propose a PTAS for the optimal online policy for  ... 
arXiv:1807.05477v1 fatcat:iedibio4bbca5iqiadxrkhggni

Online Contention Resolution Schemes [article]

Moran Feldman, Ola Svensson, Rico Zenklusen
2015 arXiv   pre-print
Our rounding technique, which we call online contention resolution schemes (OCRSs), is applicable to many online selection problems, including Bayesian online selection, oblivious posted pricing mechanisms  ...  Furthermore, we resolve two open problems from the literature; namely, we present the first constant-factor constrained oblivious posted price mechanism for matroid constraints, and the first constant-factor  ...  Let x ∈ P be an optimal solution to (4). We now create an algorithm for the Bayesian online selection problem based on the point x ∈ P and the c-selectable OCRS for P which exists by assumption.  ... 
arXiv:1508.00142v2 fatcat:qoestynocvagbi6vgx325atfwi

Physics-guided deep learning framework for predictive modeling of the Reynolds stress anisotropy [article]

Chao Jiang
2021 arXiv   pre-print
Specifically, a version with pre-constrained integrity basis is provided to demonstrate detailedly how to integrate domain-knowledge, how to design a fair and robust training strategy, and how to evaluate  ...  Recently, emerging machine learning techniques are making promising impact in turbulence modeling, but in their infancy for widespread industrial adoption.  ...  of trainable network parameters; (ii) design a reasonable objective-function to fully consider targeted needs; and (iii) select a good optimization algorithm to determine the trainable parameters.The main  ... 
arXiv:2102.03767v4 fatcat:e3ckvox72jgwzj4qnc5axckmfi

From cognitivism to autopoiesis: towards a computational framework for the embodied mind

Micah Allen, Karl J. Friston
2016 Synthese  
The FEP thus furnishes empirically productive process theories (e.g., predictive processing) by which to guide discovery through the formal modelling of the embodied mind.  ...  , internalistic mental representation-to more moderate views emphasizing the importance of 'bodyrepresentations', and finally to those which fit comfortably with radically enactive, embodied, and dynamic  ...  The authors thank Shaun Gallagher, Erik Rietveld, Sasha Ondobaka, Francesca Fardo, and Jelle Bruineberg for insightful discussion and comments on early drafts of this manuscript.  ... 
doi:10.1007/s11229-016-1288-5 pmid:29887647 pmcid:PMC5972168 fatcat:7ocscmfiafa5tjhxrezb7al3nm

Machine Learning for Fluid Mechanics

Steven L. Brunton, Bernd R. Noack, Petros Koumoutsakos
2019 Annual Review of Fluid Mechanics  
Moreover, ML algorithms can augment domain knowledge and automate tasks related to flow control and optimization.  ...  We outline fundamental ML methodologies and discuss their uses for understanding, modeling, optimizing, and controlling fluid flows.  ...  The CMA-ES is closely related to several other algorithms, such as mixed Bayesian optimization algorithms (Pelikan et al. 2004) , and the reader is referred to Kern et al. (2004) for a comparative review  ... 
doi:10.1146/annurev-fluid-010719-060214 fatcat:j6ghhpilorayfceysakiwxqgri

Average-Payoff Reinforcement Learning [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
A Bayesian Model The query by committee algorithm (Seung et al. 1992 ) is based on a Bayesian view of active learning.  ...  Jin and Si (2003) describe a Bayesian method for selecting informative items to recommend when learning a collaborative filtering model, and Steck and Jaakkola (2002) describe a method best described as  ...  In contrast, the R-MAX algorithm implicitly chooses between exploration and exploitation by using the principle of "optimism under uncertainty" .  ... 
doi:10.1007/978-1-4899-7687-1_100029 fatcat:jub4ulyg45abnf4qgutimczie4

A/B Testing [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
A Bayesian Model The query by committee algorithm (Seung et al. 1992 ) is based on a Bayesian view of active learning.  ...  Jin and Si (2003) describe a Bayesian method for selecting informative items to recommend when learning a collaborative filtering model, and Steck and Jaakkola (2002) describe a method best described as  ...  In contrast, the R-MAX algorithm implicitly chooses between exploration and exploitation by using the principle of "optimism under uncertainty" .  ... 
doi:10.1007/978-1-4899-7687-1_100507 fatcat:bg6sszljsrax5heho4glbcbicu

Repetition priming and repetition suppression: Multiple mechanisms in need of testing

Stephen J. Gotts, Carson C. Chow, Alex Martin
2012 Cognitive Neuroscience  
In one sense, the Bayesian brain hypothesis is almost certainly correct-in the sense that our capacity for near-optimal perceptual inference means that we must be performing some form of approximate Bayesian  ...  It is likely that processes of natural selection discovered solutions that optimize both performance and energy use simultaneously (e.g., Aiello & Wheeler, 1995; Allman, 1990) .  ... 
doi:10.1080/17588928.2012.697054 pmid:24171755 fatcat:yeh7cvefwnhuraht5jfmpb73ly

Explaining away repetition effects via predictive coding

Michael P. Ewbank, Richard N. Henson
2012 Cognitive Neuroscience  
In one sense, the Bayesian brain hypothesis is almost certainly correct-in the sense that our capacity for near-optimal perceptual inference means that we must be performing some form of approximate Bayesian  ...  It is likely that processes of natural selection discovered solutions that optimize both performance and energy use simultaneously (e.g., Aiello & Wheeler, 1995; Allman, 1990) .  ... 
doi:10.1080/17588928.2012.689960 pmid:24171747 fatcat:paxn2wrv45cv3gd3l7rjttilme

Reports about 8 selected benchmark cases of model hierarchies

Naomi Auer, PatriciaI Barral, Jean-David Benamou, Andreas Baermann, Andreas Binder, Daniel Fernández Comesaña, Michele Girfoglio, Lena Hauberg-Lotte, Michael Hintermüller, Wilbert Ijzerman, Onkar Jadhav, Karl Knall (+18 others)
2020 Zenodo  
These will be equipped with publically available data and will be used for training in modelling, model testing, reduced order modelling, error estimation, efficiency optimization in algorithmic approaches  ...  Based on the multitude of industrial applications, benchmarks for model hierarchies will be created that will form a basis for the interdisciplinary research and for the training programme.  ...  , production logistics optimization, transport logistics optimization and industry optimization.  ... 
doi:10.5281/zenodo.3888124 fatcat:voi4sve7jbdctctgkciykv6fz4

Reports about 8 selected benchmark cases of model hierarchies

Naomi Auer, PatriciaI Barral, Jean-David Benamou, Andreas Baermann, Andreas Binder, Daniel Fernández Comesaña, Michele Girfoglio, Lena Hauberg-Lotte, Michael Hintermüller, Wilbert Ijzerman, Onkar Jadhav, Karl Knall (+18 others)
2019 Zenodo  
These will be equipped with publically available data and will be used for training in modelling, model testing, reduced order modelling, error estimation, efficiency optimization in algorithmic approaches  ...  Based on the multitude of industrial applications, benchmarks for model hierarchies will be created that will form a basis for the interdisciplinary research and for the training programme.  ...  , production logistics optimization, transport logistics optimization and industry optimization.  ... 
doi:10.5281/zenodo.3474937 fatcat:hllwvgmppngkhedtp5uthyto6m

Structure-oriented prediction in complex networks

Zhuo-Ming Ren, An Zeng, Yi-Cheng Zhang
2018 Physics reports  
Manuel Sebastian Mariani for fruitful discussion.  ...  These approaches mainly focus on two aspects: regression problem of the stock price and prediction problem of the turning points of stock price, for instance a new feature construction approach for status  ...  Then, MSTs are extracted by using Prim's algorithm [184] or consider their equilibrium properties and transfer to an optimization problem [185] . Kim et al.  ... 
doi:10.1016/j.physrep.2018.05.002 fatcat:osv22m53ajbrdplecabirkvtcm

Symbolic Models and Emergent Models: A Review

Juyang Weng
2012 IEEE Transactions on Autonomous Mental Development  
There exists a large conceptual gap between symbolic models and emergent models for the mind.  ...  A fundamental challenge for emergent models is abstraction, which symbolic models enjoy through human handcrafting. The term abstract refers to properties disassociated with any particular form.  ...  Their incremental optimization algorithm [70] generated an image-intensity mapped 3-D world map associated with estimated viewer locations and poses along the trajectory.  ... 
doi:10.1109/tamd.2011.2159113 fatcat:op2i6qg2frc6fcflzg7g5lv4uy

A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM)

J. Mazziotta, A. Toga, A. Evans, P. Fox, J. Lancaster, K. Zilles, R. Woods, T. Paus, G. Simpson, B. Pike, C. Holmes, L. Collins (+15 others)
2001 Philosophical Transactions of the Royal Society of London. Biological Sciences  
The philosophy, strategy, algorithm development, data acquisition techniques and validation methods are described in this report along with database structures.  ...  Through an International Consortium for Brain Mapping (ICBM) a dataset is being collected that includes 7000 subjects between the ages of eighteen and ninety years and including 342 mono-and dizygotic  ...  and the National Institute for Neurological Disease and Stroke.  ... 
doi:10.1098/rstb.2001.0915 pmid:11545704 pmcid:PMC1088516 fatcat:xwrniyeggnfkxgfucgbumn4jvy

Meshless techniques for anisotropic diffusion

Annamaria Mazzia, Giorgio Pini, Flavio Sartoretto
2014 Applied Mathematics and Computation  
A good numerical method would be locally mass conservative, produce no or minimal over/under-shoots, produce minimal numerical diffusion, and require no CFL time-step limit for stability.  ...  Moreover, it would be good for the methods to be of high order accuracy.  ...  We show how to solve the equations using a global implicit approach in an efficient way, and we present the derived computational results.  ... 
doi:10.1016/j.amc.2014.03.032 fatcat:c527226gyfgbffnq4p67qxd7wi
« Previous Showing results 1 — 15 out of 75 results