Journal ArticleDOI
Pattern Recognition and Machine Learning
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TLDR
This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.Abstract:
(2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.read more
Citations
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Proceedings Article
Analysis of i-vector Length Normalization in Speaker Recognition Systems.
TL;DR: The proposed approach deals with the nonGaussian behavior of i-vectors by performing a simple length normalization, which allows the use of probabilistic models with Gaussian assumptions that yield equivalent performance to that of more complicated systems based on Heavy-Tailed assumptions.
Proceedings ArticleDOI
End-to-end scene text recognition
TL;DR: While scene text recognition has generally been treated with highly domain-specific methods, the results demonstrate the suitability of applying generic computer vision methods.
Journal ArticleDOI
Structural topic models for open ended survey responses
Margaret E. Roberts,Brandon M. Stewart,Dustin Tingley,Chris Lucas,Jetson Leder-Luis,Shana Kushner Gadarian,Bethany Albertson,David G. Rand +7 more
TL;DR: The structural topic model makes analyzing open-ended responses easier, more revealing, and capable of being used to estimate treatment effects, and is illustrated with analysis of text from surveys and experiments.
Posted Content
Denoising Diffusion Implicit Models
TL;DR: Denoising diffusion implicit models (DDIMs) are presented, a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs that can produce high quality samples faster and perform semantically meaningful image interpolation directly in the latent space.
Proceedings ArticleDOI
Exploiting geographical influence for collaborative point-of-interest recommendation
TL;DR: This paper argues that the geographical influence among POIs plays an important role in user check-in behaviors and model it by power law distribution, and develops a collaborative recommendation algorithm based on geographical influence based on naive Bayesian.