Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning

By Michalis K. Titsias, Miguel L'azaro-Gredilla. 2011

212 citations recorded by the source corpus.

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Citations in the corpus

A Survey on Multi-Task Learning (2017, methodology): “Zhu et al. [14] propose a 4 multi-task inﬁnite support vector machine via the Indian buffet process and the difference is that in [14] the dictionary is sparse and model parameters are non-sparse. In [15], the spike and slab prior is used to learn sparse model parameters for multi-output regression ”
Large-Scale Feature Learning With Spike-and-Slab Sparse Coding (2012, background): “peared a number of times in different domains (Lücke and Sheikh, 2011; Garrigues and Olshausen, 2008; Mohamed et al., 2011; Zhou et al., 2009; Titsias and Lázaro-Gredilla, 2011).”
Spike-and-Slab Sparse Coding for Unsupervised Feature Discovery (2012, methodology): “…supervised, semi-supervised and selftaught learning, the basic form of the S3C model (i.e. a spike-and-slab latent factor model) has appeared a number of times in different domains (Lücke and Sheikh, 2011; Garrigues and Olshausen, 2008; Mohamed et al., 2011; Titsias and Lázaro-Gredilla, 2011).”
Asymmetric Transfer Learning with Deep Gaussian Processes (2015, background): “• The resultant model both projects input data onto a latent manifold (first-layer GP) and maps this projection to the output space (second-layer GP) non-linearly.”
Robustly representing inferential uncertainty in deep neural networks through sampling (2016, unknown): “In addition to Bernoulli and Gaussian distributions, spike-and-slab distributions, a combination of the two, have been investigated, particularly for linear models [20; 19; 6; 10].”
Scaling Up Spike-and-Slab Models for Unsupervised Feature Learning (2013, methodology): “This is for example the approach taken by Titsias and Lázaro-Gredilla (2011) who independently developed a variational inference procedure for the same problem.”