scholarly journals Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank

2020 ◽  
Author(s):  
Ali Vardasbi ◽  
Maarten de Rijke ◽  
Ilya Markov
Keyword(s):  
Learning To Rank ◽  
Cascade Model ◽  
Model Based

Deep Cascade Model-Based Face Recognition: When Deep-Layered Learning Meets Small Data

2020 ◽  
Vol 29 ◽  
pp. 1016-1029 ◽  
Author(s):  
Lei Zhang ◽  
Ji Liu ◽  
Bob Zhang ◽  
David Zhang ◽  
Ce Zhu
Keyword(s):  
Face Recognition ◽  
Cascade Model ◽  
Small Data ◽  
Model Based ◽  
Layered Learning

scholarly journals Dynamic cascade-model-based frequency-scanning interferometry for real-time and rapid absolute optical ranging

2019 ◽  
Vol 27 (15) ◽  
pp. 21929 ◽  
Author(s):  
Zhongwen Deng ◽  
Zhigang Liu ◽  
Xingyu Jia ◽  
Wen Deng ◽  
Xin Zhang
Keyword(s):  
Real Time ◽  
Cascade Model ◽  
Model Based ◽  
Frequency Scanning ◽  
Frequency Scanning Interferometry

scholarly journals Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model

2019 ◽  
Author(s):  
Chang Li ◽  
Maarten de Rijke
Keyword(s):  
Online Learning ◽  
Lower Bound ◽  
Web Search ◽  
Learning To Rank ◽  
Stationary Problem ◽  
Upper Bounds ◽  
Cascade Model ◽  
User Preferences ◽  
Logarithmic Factor ◽  
Ranked List

Non-stationarity appears in many online applications such as web search and advertising. In this paper, we study the online learning to rank problem in a non-stationary environment where user preferences change abruptly at an unknown moment in time. We consider the problem of identifying the K most attractive items and propose cascading non-stationary bandits, an online learning variant of the cascading model, where a user browses a ranked list from top to bottom and clicks on the first attractive item. We propose two algorithms for solving this non-stationary problem: CascadeDUCB and CascadeSWUCB. We analyze their performance and derive gap-dependent upper bounds on the n-step regret of these algorithms. We also establish a lower bound on the regret for cascading non-stationary bandits and show that both algorithms match the lower bound up to a logarithmic factor. Finally, we evaluate their performance on a real-world web search click dataset.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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