Infinite Latent SVM for Classification and Multi-task Learning

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Survey on Multi-Task Learning (2017, background): “via the Indian buffet process and the difference is that in [13] the dictionary is sparse and model parameters are nonsparse.”
Learning Answer-Entailing Structures for Machine Comprehension (2015, methodology): “This is much simpler than other competing approaches such as Zhu et al. (2011) proposed in the literature for multi-task LSSVM.”
Max-Margin Deep Generative Models (2015, background): “The recent work on learning max-margin generative models includes max-margin Harmoniums [3], maxmargin topic models [33], and nonparametric Bayesian latent SVMs [34] which can infer the dimension of latent features from data.”
MedLDA: maximum margin supervised topic models (2012, methodology): “…the present work, we have successfully applied this operator to other challenging settings of learning latent variable structured prediction models with nontrivial dependence structures among output variables (Zhu et al., 2008) and learning nonparametric Bayesian models (Zhu et al., 2011a,b).”
On Better Exploring and Exploiting Task Relationships in Multitask Learning: Joint Model and Feature Learning (2018, background): “As one of the most popular single-task learning methods, a support vector machine (SVM) has been studied in many MTL works [5], [12], [25]–[30].”