Journal ArticleDOI
Improving predictive inference under covariate shift by weighting the log-likelihood function
TLDR
A class of predictive densities is derived by weighting the observed samples in maximizing the log-likelihood function, effective in cases such as sample surveys or design of experiments, where the observed covariate follows a different distribution than that in the whole population.About:
This article is published in Journal of Statistical Planning and Inference.The article was published on 2000-10-01. It has received 1767 citations till now. The article focuses on the topics: Covariate & Likelihood function.read more
Citations
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Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
Journal ArticleDOI
A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
Posted Content
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Batch Normalization as mentioned in this paper normalizes layer inputs for each training mini-batch to reduce the internal covariate shift in deep neural networks, and achieves state-of-the-art performance on ImageNet.
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Unsupervised Domain Adaptation by Backpropagation
Yaroslav Ganin,Victor Lempitsky +1 more
TL;DR: In this paper, a gradient reversal layer is proposed to promote the emergence of deep features that are discriminative for the main learning task on the source domain and invariant with respect to the shift between the domains.
Journal ArticleDOI
A survey of transfer learning
TL;DR: This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied toTransfer learning, which can be applied to big data environments.
References
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Journal ArticleDOI
A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
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Maximum likelihood estimation of misspecified models
TL;DR: In this article, the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference are examined, and the properties of the quasi-maximum likelihood estimator and the information matrix are exploited to yield several useful tests.
Book
Robust statistics: the approach based on influence functions
TL;DR: This paper presents a meta-modelling framework for estimating the values of Covariance Matrices and Multivariate Location using one-Dimensional and Multidimensional Estimators.
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