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Proceedings ArticleDOI
A Survey of Recent Advances in Transfer Learning
Huan Liang,Wenlong Fu,Fengji Yi +2 more
TL;DR: This paper focuses on sorting and classifying the integrated results of the transfer learning and the other non-transfer machine learning, such as reinforcement leaning, lifelong learning, adversarial networks, and categorizes the relevant applications for transfer learning.
Journal ArticleDOI
Network Structure and Observational Learning: Evidence from a Location-Based Social Network
Zhan Shi,Andrew B. Whinston +1 more
TL;DR: It is hypothesized that the "check-ins" made by friends help users learn the potential payoff of visiting a venue, and it is shown that weighting the friends' check-ins by a parsimonious proximity measure can yield a more intuitive result than the plain proportion does.
Book ChapterDOI
Deep Recurrent Networks for Separation and Recognition of Single-Channel Speech in Nonstationary Background Audio
TL;DR: This work compares the performance of deep computational architectures with conventional statistical techniques as well as variants of nonnegative matrix factorization, and establishes that one can achieve impressively superior results with deep-learning-based techniques on this problem.
Journal ArticleDOI
Spectral clustering of high-dimensional data exploiting sparse representation vectors
Sen Wu,Xiaodong Feng,Wenjun Zhou +2 more
TL;DR: Experimental results on several real-world, high-dimensional datasets demonstrate that spectral clustering based on the proposed weight matrices outperforms existing spectral clusterencing algorithms, which use sparse coefficients directly.
Journal ArticleDOI
Regularized Latent Semantic Indexing: A New Approach to Large-Scale Topic Modeling
TL;DR: This article introduces Regularized Latent Semantic Indexing (RLSI)---including a batch version and an online version, referred to as batch and online RLSI, respectively---to scale up topic modeling and proposes adopting ℓ1 norm on topics andℓ2 norm on document representations to create a model with compact and readable topics and which is useful for retrieval.
Related Papers (5)
Learning the parts of objects by non-negative matrix factorization
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Pentti Paatero,Unto Tapper +1 more