## Accelerated training of conditional random fields with stochastic gradient methods (2006)

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Venue: | In ICML |

Citations: | 101 - 4 self |

### BibTeX

@INPROCEEDINGS{Vishwanathan06acceleratedtraining,

author = {S. V. N. Vishwanathan and Nicol N. Schraudolph and Mark W. Schmidt and Kevin P. Murphy},

title = {Accelerated training of conditional random fields with stochastic gradient methods},

booktitle = {In ICML},

year = {2006},

pages = {969--976}

}

### Years of Citing Articles

### OpenURL

### Abstract

We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs). On several large data sets, the resulting optimizer converges to the same quality of solution over an order of magnitude faster than limited-memory BFGS, the leading method reported to date. We report results for both exact and inexact inference techniques. 1.

### Citations

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Citation Context ...eported to date. We report results for both exact and inexact inference techniques. 1. Introduction Conditional Random Fields (CRFs) have recently gained popularity in the machine learning community (=-=Lafferty et al., 2001-=-; Sha & Pereira, 2003; Kumar & Hebert, 2004). Current training methods for CRFs 1 include generalized iterative scaling (GIS), conjugate gradient (CG), and limited-memory BFGS. These are all batch-onl... |

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Citation Context ...od than the MF free energy (Weiss, 2001). Although LBP can sometimes oscillate, convergent versions have been developed (e.g., Kolmogorov, 2004). For some kinds of potentials, one can use graph cuts (=-=Boykov et al., 2001-=-) to find an approximate MAP estimate of the labels, which can be used inside a Viterbi training procedure. However, this produces a very discontinuous estimate of the gradient (though one could presu... |

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402 | Tarjan, A separator theorem for planar graphs
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Citation Context ... a far slower rate. We also obtained comparable results (not reported here) with a similar setup on the first BioCreAtivE (Critical Assessment of Information Extraction in Biology) challenge task 1A (=-=Hirschman et al., 2005-=-). 5. Experiments on 2D Lattice CRFs For the 2D CRF experiments we compare four optimization algorithms: SGD, SMD, BFGS as implemented in Matlab’s fminunc function (with ‘largeScale’ set to ‘off’), an... |

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Citation Context ...ndom permutations of the data substantially identical results to those reported below. 4.1. CoNLL-2000 Base NP Chunking Task Our first experiment uses the well-known CoNLL-2000 Base NP chunking task (=-=Sang & Buchholz, 2000-=-). Text 2 Available under LGPL from http://chasen.org/ ∼ taku/software/CRF++/. Our modified code, as well as the data sets, configuration files, and results for all experiments reported here will be a... |

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Citation Context ...exact and inexact inference techniques. 1. Introduction Conditional Random Fields (CRFs) have recently gained popularity in the machine learning community (Lafferty et al., 2001; Sha & Pereira, 2003; =-=Kumar & Hebert, 2004-=-). Current training methods for CRFs 1 include generalized iterative scaling (GIS), conjugate gradient (CG), and limited-memory BFGS. These are all batch-only algorithms that do not work well in an on... |

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Citation Context ...elerate this process by using second-order information to adapt the gradient step sizes (Schraudolph, 1999, 2002). Key to SMD’s efficiency is the implicit computation of fast Hessian-vector products (=-=Pearlmutter, 1994-=-; Griewank, 2000). In this paper we marry the above two techniques and show how SMD can be used to significantly accelerate the training of CRFs. The rest of the paper is organized as follows: Section... |

62 | Local Gain Adaptation in Stochastic Gradient Descend
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(Show Context)
Citation Context ...vergence to the optimum is often painfully slow. Gain adaptation methods like Stochastic Meta-Descent (SMD) accelerate this process by using second-order information to adapt the gradient step sizes (=-=Schraudolph, 1999-=-, 2002). Key to SMD’s efficiency is the implicit computation of fast Hessian-vector products (Pearlmutter, 1994; Griewank, 2000). In this paper we marry the above two techniques and show how SMD can b... |

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15 |
Learning in Markov random fields: An empirical study
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Citation Context ... N = k × k grid, for instance, is w = O(2k) (Lipton & Tarjan, 1979), so exact inference takes O(| Y | 2k ) time. Various approximate inference methods have been used in parameter learning algorithms (=-=Parise & Welling, 2005-=-). Here we consider two of the simplest: mean field (MF) and loopy belief propagation (LBP) (Weiss, 2001; Yedidia et al., 2003). The MF free energy is a lower bound on the loglikelihood, and hence an ... |

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Citation Context ...d to be computationally most efficient. Unfortunately most advanced gradient methods do not tolerate the sampling noise inherent in stochastic approximation: it collapses conjugate search directions (=-=Schraudolph & Graepel, 2003-=-) and confuses the line searches that both conjugate gradient and quasi-Newton methods depend upon. Full secondorder methods are unattractive here because the computational cost of inverting the Hessi... |

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1 |
Overview of BioCreAtivE:critical assessment of information extraction for biology
- Hirschman, Yeh, et al.
- 2005
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Citation Context ... a far slower rate. We also obtained comparable results (not reported here) with a similar setup on the first BioCreAtivE (Critical Assessment of Information Extraction in Biology) challenge task 1A (=-=Hirschman et al., 2005-=-). 5. Experiments on 2D Lattice CRFs For the 2D CRF experiments we compare four optimization algorithms: SGD, SMD, BFGS as implemented in Matlab’s fminunc function (with ‘largeScale’ set to ‘off’), an... |

1 | Biomedical named intity recognition using conditional random fields and rich feature sets - Settles - 2004 |

1 |
Image Analysis, Random Fields and Accelerated Training of CRFs with Stochastic Gradient Methods Dynamic Monte Carlo Methods
- Winkler
- 1995
(Show Context)
Citation Context ...y a good approximation to the likelihood, as the amount of training data (or the size of the lattice, when using tied parameters) tends to infinity, its maximum coincides with that of the likelihood (=-=Winkler, 1995-=-). Note that pseudo-likelihood estimates the parameters conditional on i’s neighbors being observed. As a consequence, PL tends to place too much emphasis on the edge potentials, and not enough on the... |