Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Fast exact multiplication by the Hessian
Neural Computation
Relational learning with statistical predicate invention: better models for hypertext
Machine Learning - Special issue on inducive logic programming
Training products of experts by minimizing contrastive divergence
Neural Computation
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Learning the structure of Markov logic networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Entity Resolution with Markov Logic
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Discriminative training of Markov logic networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Discriminative Structure Learning of Markov Logic Networks
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
A Generalized Joint Inference Approach for Citation Matching
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Just Add Weights: Markov Logic for the Semantic Web
Uncertainty Reasoning for the Semantic Web I
Efficient MAP Inference for Statistical Relational Models through Hybrid Metaheuristics
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Learning to Disambiguate Search Queries from Short Sessions
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Max-Margin Weight Learning for Markov Logic Networks
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
High Performing Algorithms for MAP and Conditional Inference in Markov Logic
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Joint inference for knowledge extraction from biomedical literature
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Learning complex action models with quantifiers and logical implications
Artificial Intelligence
Speeding up inference in statistical relational learning by clustering similar query literals
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Policy transfer via Markov logic networks
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Generative Structure Learning for Markov Logic Networks
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Discriminative Markov logic network structure learning based on propositionalization and X2-test
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Boosting learning and inference in Markov logic through metaheuristics
Applied Intelligence
Journal of Biomedical Informatics
On the combination of logical and probabilistic models for information analysis
Applied Intelligence
Discriminative learning of first-order weighted abduction from partial discourse explanations
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Programming with personalized pagerank: a locally groundable first-order probabilistic logic
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Integration of large scale knowledge bases using probabilistic graphical models
Proceedings of the 7th ACM international conference on Web search and data mining
Expectation maximization over binary decision diagrams for probabilistic logic programs
Intelligent Data Analysis
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Markov logic networks (MLNs) combine Markov networks and first-order logic, and are a powerful and increasingly popular representation for statistical relational learning. The state-of-the-art method for discriminative learning of MLN weights is the voted perceptron algorithm, which is essentially gradient descent with an MPE approximation to the expected sufficient statistics (true clause counts). Unfortunately, these can vary widely between clauses, causing the learning problem to be highly ill-conditioned, and making gradient descent very slow. In this paper, we explore several alternatives, from per-weight learning rates to second-order methods. In particular, we focus on two approaches that avoid computing the partition function: diagonal Newton and scaled conjugate gradient. In experiments on standard SRL datasets, we obtain order-of-magnitude speedups, or more accurate models given comparable learning times.