Structured Sparsity in Structured Prediction

By André F. T. Martins, Noah A. Smith, Mário A. T. Figueiredo, Pedro M. Q. Aguiar. 2011

77 citations recorded by the source corpus.

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In Optimization & Training

Citations in the corpus

Frank-Wolfe Algorithms for Saddle Point Problems (2016, methodology): “For example, we could use a structured sparsity inducing norm (Martins et al., 2011) for the parameter x , such as the overlapping group lasso for which the projection is expensive (Bach et al., 2012), while Y could be a combinatorial object such as a the ground state of a planar Ising model…”
Optimization with Sparsity-Inducing Penalties (2011, background): “More recently, this hierarchical penalty was proved to be efficient for template selection in natural language processing [92].”
RNN Architecture Learning with Sparse Regularization (2019, methodology): “Lin, 2006), has been proposed as a method to reduce model size while preserving performance (Martins et al., 2011).”
Structured Variable Selection with Sparsity-Inducing Norms (2009, background): “, 2011b) and for template selection in natural language processing (Martins et al., 2011).”
Linguistic Structured Sparsity in Text Categorization (2014, methodology): “Group lasso has been shown useful in a range of applications, including computational biology (Kim and Xing, 2008), signal processing (Lv et al., 2011), and NLP (Eisenstein et al., 2011; Martins et al., 2011; Nelakanti et al., 2013).”
Structure Regularization for Structured Prediction (2014, methodology): “recent studies on structured sparsity in structured prediction scenarios [11, 8], via adopting mixed norm regularization [10], Group Lasso [22], and posterior regularization [5].”
“Love ya, jerkface”: Using Sparse Log-Linear Models to Build Positive and Impolite Relationships with Teens (2012, background): “On the other hand, recent studies on `1/`2 based group penalty for evaluating dialogue systems (González-Brenes and Mostow, 2011), structured sparsity for linguistic structure prediction (Martins et al., 2011), and discovering historical legal opinions with a sparse mixed-effects latent variable mod”
Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers (2014, methodology): “Indeed, Martins et al. (2011a) introduced a proximal gradient algorithm for handling overlapping group lasso.”