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Structured prediction requires searching over a combinatorial number of structures.
Tres observaciones sobre el algebra lineal
Birkhoff, G · 1946
Earlier work this paper cites.
On conjugate convex functions
Fenchel, W · 1949
Earlier work this paper cites.
The Hungarian method for the assignment problem
Kuhn, H. W · 1955
Earlier work this paper cites.
An algorithm for quadratic programming
Frank, M. and Wolfe, P · 1956
Earlier work this paper cites.
On the shortest arborescence of a directed graph
Chu, Y.-J. and Liu, T.-H · 1965
Earlier work this paper cites.
Optimum branchings
Edmonds, J · 1967
Earlier work this paper cites.
Finding the nearest point in a polytope
Wolfe, P · 1976
Earlier work this paper cites.
The complexity of computing the permanent
Valiant, L. G · 1979
Earlier work this paper cites.
The k k best spanning arborescences of a network
Camerini, P. M., Fratta, L., and Maffioli, F · 1980
Earlier work this paper cites.
Algorithms for finding K K -best perfect matchings
Chegireddy, C. R. and Hamacher, H. W · 1987
Earlier work this paper cites.
A shortest augmenting path algorithm for dense and sparse linear assignment problems
Jonker, R. and Volgenant, A · 1987
Earlier work this paper cites.
A tutorial on Hidden Markov Models and selected applications in speech recognition
Rabiner, L. R · 1989
Earlier work this paper cites.
Optimization and Nonsmooth Analysis
Clarke, F. H · 1990
Earlier work this paper cites.
Numerical Optimization
Nocedal, J. and Wright, S · 1999
Earlier work this paper cites.
Factor graphs and the sum-product algorithm
Kschischang, F. R., Frey, B. J., and Loeliger, H.-A · 2001
Earlier work this paper cites.
Conditional Random Fields: Probabilistic models for segmenting and labeling sequence data
Lafferty, J. D., McCallum, A., and Pereira, F. C. N · 2001
Earlier work this paper cites.
Discriminative training methods for Hidden Markov Models: Theory and experiments with perceptron algorithms
Collins, M · 2002
Earlier work this paper cites.
Max-Margin Markov Networks
Taskar, B., Guestrin, C., and Koller, D · 2003
Earlier work this paper cites.
Convex Optimization
Boyd, S. and Vandenberghe, L · 2004
Earlier work this paper cites.
Learning Structured Prediction Models: A Large Margin Approach
Taskar, B · 2004
Earlier work this paper cites.
Support vector machine learning for interdependent and structured output spaces
Tsochantaridis, I., Hofmann, T., Joachims, T., and Altun, Y · 2004
Earlier work this paper cites.
Finding the M M most probable configurations using loopy belief propagation
Yanover, C. and Weiss, Y · 2004
Earlier work this paper cites.
Online large-margin training of dependency parsers
McDonald, R., Crammer, K., and Pereira, F · 2005
Earlier work this paper cites.
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Nivre, J., de Marneffe, M.-C., Ginter, F., Goldberg, Y., Hajic, J., Manning, C. D., McDonald, R. T., Petrov, S., Pyysalo, S., Silveira, N., et al · 2016
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