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Structured Sparsity in Structured Prediction Andre F. T. Martins
 

Summary: Structured Sparsity in Structured Prediction
Andr´e F. T. Martins
Noah A. Smith
Pedro M. Q. Aguiar
M´ario A. T. Figueiredo

School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA

Instituto de Sistemas e Rob´otica, Instituto Superior T´ecnico, Lisboa, Portugal

Instituto de Telecomunicac¸~oes, Instituto Superior T´ecnico, Lisboa, Portugal
{afm,nasmith}@cs.cmu.edu, aguiar@isr.ist.utl.pt, mtf@lx.it.pt
Abstract
Linear models have enjoyed great success in
structured prediction in NLP. While a lot of
progress has been made on efficient train-
ing with several loss functions, the problem
of endowing learners with a mechanism for
feature selection is still unsolved. Common
approaches employ ad hoc filtering or L1-

  

Source: Aguiar, Pedro M. Q. - Institute for Systems and Robotics (Lisbon)

 

Collections: Engineering