167 Hits in 17.0 sec

Capturing heterogeneous group differences using mixture-of-experts: Application to a study of aging

Harini Eavani, Meng Kang Hsieh, Yang An, Guray Erus, Lori Beason-Held, Susan Resnick, Christos Davatzikos
2016 NeuroImage  
In this paper, we present a method that explicitly models and captures heterogeneous patterns of change in the affected group relative to a reference group of controls.  ...  In the case of patient/control comparisons, each such pattern aims to capture a different dimension of a disease, and hence to identify patient subgroups.  ...  Acknowledgments This study was supported in part by NIH grant AG014971, the Intramural Research Program, National Institute on Aging, and NIH contract HHSN2712013000284P by the NIA to UPenn.  ... 
doi:10.1016/j.neuroimage.2015.10.045 pmid:26525656 pmcid:PMC5460911 fatcat:knqmxjzurrfkde7vl73qgvoelu

Mixtures of Experts Models [article]

Isobel Claire Gormley, Sylvia Frühwirth-Schnatter
2018 arXiv   pre-print
Given their mixture model foundation, mixtures of experts models possess a diverse range of analytic uses, from clustering observations to capturing parameter heterogeneity in cross-sectional data.  ...  Mixtures of experts models provide a framework in which covariates may be included in mixture models.  ...  Their demonstrated use to cluster observations, and to appropriately capture heterogeneity in cross sectional data, provides only a glimpse of their potential flexibility and utility in a wide range of  ... 
arXiv:1806.08200v1 fatcat:amaewljxlveu5hkpdaeujyqlbu

Learning to Adapt Clinical Sequences with Residual Mixture of Experts [article]

Jeong Min Lee, Milos Hauskrecht
2022 arXiv   pre-print
In this work, we aim to alleviate this limitation by refining a one-fits-all model using a Mixture-of-Experts (MoE) architecture.  ...  With this way, the mixture of experts can provide flexible adaptation to the (limited) predictive power of the single base RNN model.  ...  The key idea is to specialize the Mixture-of-Experts to learn the residual that δ base cannot capture.  ... 
arXiv:2204.02687v1 fatcat:yjhkfsxc7jcyldroeb4tsri3ce

M3E2: Multi-gate Mixture-of-experts for Multi-treatment Effect Estimation [article]

Raquel Aoki, Yizhou Chen, Martin Ester
2022 arXiv   pre-print
This work proposes the M3E2, a multi-task learning neural network model to estimate the effect of multiple treatments.  ...  In contrast to existing methods, M3E2 can handle multiple treatment effects applied simultaneously to the same unit, continuous and binary treatments, and many covariates.  ...  In a multi-gate mixture-of-expert (MMoE) architecture [15] , a hard-parameter sharing network can be interpreted as a single expert model.  ... 
arXiv:2112.07574v2 fatcat:nwflmz2bffcgve2co4n4agfeoa

Anchoring to Exemplars for Training Mixture-of-Expert Cell Embeddings [article]

Siqi Wang, Manyuan Lu, Nikita Moshkov, Juan C. Caicedo, Bryan A. Plummer
2021 arXiv   pre-print
We propose Treatment ExemplArs with Mixture-of-experts (TEAMs), an embedding learning approach that learns a set of experts that are specialized in capturing technical variations in our training set and  ...  equipments used to collect microscopy images.  ...  In contrast, we use a mixture-of-experts approach that obtained using a single linear projection.  ... 
arXiv:2112.03208v1 fatcat:dem557uqwjavboekwtwimk66hi

Scenario Adaptive Mixture-of-Experts for Promotion-Aware Click-Through Rate Prediction [article]

Xiaofeng Pan, Yibin Shen, Jing Zhang, Keren Yu, Hong Wen, Shui Liu, Chengjun Mao, Bo Cao
2022 arXiv   pre-print
Technically, it follows the idea of Mixture-of-Experts by adopting multiple experts to learn feature representations, which are modulated by a Feature Gated Network (FGN) via an attention mechanism.  ...  In this work, we propose Scenario Adaptive Mixture-of-Experts (SAME), a simple yet effective model that serves both promotion and normal scenarios.  ...  Partially inspired by these prior works, we borrow the idea of Mixture-of-Experts.  ... 
arXiv:2112.13747v2 fatcat:qu7qtomqfbc47j4wrwbb2d7quq

MECATS: Mixture-of-Experts for Quantile Forecasts of Aggregated Time Series [article]

Xing Han, Jing Hu, Joydeep Ghosh
2021 arXiv   pre-print
We introduce a mixture of heterogeneous experts framework called , which simultaneously forecasts the values of a set of time series that are related through an aggregation hierarchy.  ...  Different types of forecasting models can be employed as individual experts so that the form of each model can be tailored to the nature of the corresponding time series. learns hierarchical relationships  ...  Left: point prediction generated by mixture-of-experts, Lrecon is used to train gating network NNg .  ... 
arXiv:2112.11669v1 fatcat:iagqb6az4jfy7k7ktlqalvkjzy

Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering

Sylvia Frühwirth-Schnatter, Stefan Pittner, Andrea Weber, Rudolf Winter-Ebmer
2018 Annals of Applied Statistics  
In addition, a mixtureof-experts approach allows us to model the probability of belonging to a certain cluster as depending on a set of covariates via a multinomial logit model.  ...  In particular, we follow the careers of workers who experience a job displacement due to plant closure and observe -over a period of forty quarters -whether these workers manage to return to a steady career  ...  Acknowledgements The research was funded by the Austrian Science Fund (FWF): S10309-G16 (NRN "The Austrian Center for Labor Economics and the Analysis of the Welfare State") and the CD Laboratory "Ageing  ... 
doi:10.1214/17-aoas1132 fatcat:tqduem3h75h6bj3jss27b4bqrm

Labor market entry and earnings dynamics: Bayesian inference using mixtures-of-experts Markov chain clustering

Sylvia Frühwirth-Schnatter, Christoph Pamminger, Andrea Weber, Rudolf Winter-Ebmer
2011 Journal of applied econometrics  
The statistical challenge in our application comes from the difficulty in extending distance-based clustering approaches to the problem of identify groups of similar time series in a panel of discrete-valued  ...  In order to analyze group membership we present an extension to this approach by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule using a multinomial  ...  Acknowledgements This research is supported by the Austrian Science Foundation (FWF) under the grant S 10309-G14 (NRN "The Austrian Center for Labor Economics and the Analysis of the Welfare State", Subproject  ... 
doi:10.1002/jae.1249 fatcat:ark6c2kct5gpvnqadzbvgricuu

Steered Mixture-of-Experts for Light Field Images and Video: Representation and Coding

Ruben Verhack, Sikora Thomas, Glenn Van Wallendael, Peter Lambert
2019 IEEE transactions on multimedia  
We propose a novel coding framework for higher-dimensional image modalities, called Steered Mixture-of-Experts (SMoE).  ...  Index Terms-Mixture of experts, light fields, mixture models, sparse representation, bayesian modeling.  ...  of a Mixture-of-Experts with one layer for regression.  ... 
doi:10.1109/tmm.2019.2932614 fatcat:2nuvguaeorguxlkhqir6yzi27e

UKnowledge Development in Normal Mixture and Mixture of Experts Modeling

Meng Qi
Simulation study To simulate data with m per group, we need first get the joint distribution of m variables.  ...  As we mentioned in the application section, a two components normal mixture is not a good fit of the presumably correlated Z-values, this motivates us to expand the method to testing 2 versus 3 component  ...  Then according to the definition in (Van der Vaart [2000] ), we have two bracketing functions l and u with finite L(P ) -norms.  ... 

Transfer Learning from Well-Curated to Less-Resourced Populations with HIV

Sonali Parbhoo, Mario Wieser, Volker Roth, Finale Doshi-Velez
2020 Machine Learning in Health Care  
We demonstrate its utility for optimising treatments for the first time in a set of HIV patients in Africa, and note how this approach may be applicable to many other scenarios where a variable is measured  ...  In this work, we present a novel mixture based approach that uses a deep information bottleneck to transfer patterns learned from European HIV cohorts-where genomic data is readily available-to African  ...  In this paper, we adapt this framework to a multi-treatment setting, and incorporate this knowledge into a mixture-of-experts model for reasoning about treatment effects over heterogenous patient groups  ... 
dblp:conf/mlhc/ParbhooW0D20 fatcat:3fub3ihtpbhrxjkmwymafufwke

Concordance of Alzheimer's Disease Subtypes Produced from Different Representative Morphological Measures: A Comparative Study

Baiwen Zhang, Lan Lin, Lingyu Liu, Xiaoqi Shen, Shuicai Wu
2022 Brain Sciences  
However, how the two measures affect the definition of AD subtypes remains unclear. Methods: A total of 180 AD patients from the ADNI database were used to identify AD subgroups.  ...  This study provides a valuable reference for selecting features in future studies of AD subtypes.  ...  Conflicts of Interest: The authors have no competing interests to declare.  ... 
doi:10.3390/brainsci12020187 pmid:35203950 pmcid:PMC8869952 fatcat:uc2nc7fyvrcerkinazvhit7p4i

A Generalizable Speech Emotion Recognition Model Reveals Depression and Remission [article]

Lasse Hansen, Yan-Ping Zhang, Detlef Wolf, Konstantinos Sechidis, Nicolai Ladegaard, Riccardo Fusaroli
2021 bioRxiv   pre-print
Methods: A Mixture-of-Experts machine learning model was trained to infer happy/sad emotional state using three publicly available emotional speech corpora.  ...  This study investigated a generalizable approach to aid clinical evaluation of depression and remission from voice.  ...  A gradient boosted decision tree model was trained on each dataset separately to predict the probability of sounding happy or sad using Catboost 28 and combined in a Mixture of Experts (MoE) architecture  ... 
doi:10.1101/2021.09.01.458536 fatcat:ub6jhmyugrdcbeh3wsjsaclng4

Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare [article]

Ingyo Chung, Saehoon Kim, Juho Lee, Kwang Joon Kim, Sung Ju Hwang, Eunho Yang
2019 arXiv   pre-print
To this end, we propose a composite model of a deep neural network to learn complex global trends from the large number of patients, and Gaussian Processes (GP) to probabilistically model individual time-series  ...  that captures global trend across diverse patients and ii) a patient-specific component that models idiosyncratic variability for each patient.  ...  For example, the patients can be clustered into the groups having different range of ages or different range of where they live.  ... 
arXiv:1806.01551v3 fatcat:ggydqdhq25dvzcewkz7mb4efwe
« Previous Showing results 1 — 15 out of 167 results