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Deep Transfer as Structure Learning in Markov Logic Networks
"... Markov logic networks (MLNs) generalize firstorder logic and probabilistic graphical models, using weighted formulas of firstorder logic to represent relational knowledge. The deep transfer algorithm (DTM) proposed by Davis and ..."
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Cited by 1 (0 self)
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Markov logic networks (MLNs) generalize firstorder logic and probabilistic graphical models, using weighted formulas of firstorder logic to represent relational knowledge. The deep transfer algorithm (DTM) proposed by Davis and
Efficient weight learning for Markov logic networks
 In Proceedings of the Eleventh European Conference on Principles and Practice of Knowledge Discovery in Databases
, 2007
"... Abstract. Markov logic networks (MLNs) combine Markov networks and firstorder logic, and are a powerful and increasingly popular representation for statistical relational learning. The stateoftheart method for discriminative learning of MLN weights is the voted perceptron algorithm, which is ess ..."
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Cited by 87 (7 self)
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Abstract. Markov logic networks (MLNs) combine Markov networks and firstorder logic, and are a powerful and increasingly popular representation for statistical relational learning. The stateoftheart method for discriminative learning of MLN weights is the voted perceptron algorithm, which
Policy transfer via Markov logic networks
 In Proceedings of the 19th International Conference on Inductive Logic Programming (ILP09
, 2009
"... Abstract. We propose using a statisticalrelational model, the Markov Logic Network, for knowledge transfer in reinforcement learning. Our goal is to extract relational knowledge from a source task and use it to speed up learning in a related target task. We show that Markov Logic Networks are effec ..."
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Cited by 3 (0 self)
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Abstract. We propose using a statisticalrelational model, the Markov Logic Network, for knowledge transfer in reinforcement learning. Our goal is to extract relational knowledge from a source task and use it to speed up learning in a related target task. We show that Markov Logic Networks
Discriminative Structure and Parameter Learning for Markov Logic Networks
"... Markov logic networks (MLNs) are an expressive representation for statistical relational learning that generalizes both firstorder logic and graphical models. Existing methods for learning the logical structure of an MLN are not discriminative; however, many relational learning problems involve spe ..."
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Cited by 56 (5 self)
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Markov logic networks (MLNs) are an expressive representation for statistical relational learning that generalizes both firstorder logic and graphical models. Existing methods for learning the logical structure of an MLN are not discriminative; however, many relational learning problems involve
Fully Parallel Inference inMarkov Logic Networks
"... Abstract: Markov logic is apowerful tool for handling the uncertainty that arises in realworld structured data; it has been applied successfully to anumber ofdata management problems. In practice, the resulting ground Markov logic networks can get very large, which poses challenges to scalable infe ..."
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Abstract: Markov logic is apowerful tool for handling the uncertainty that arises in realworld structured data; it has been applied successfully to anumber ofdata management problems. In practice, the resulting ground Markov logic networks can get very large, which poses challenges to scalable
Transfer in Reinforcement Learning via Markov Logic Networks
"... We propose the use of statistical relational learning, and in particular the formalism of Markov Logic Networks, for transfer in reinforcement learning. Our goal is to extract relational knowledge from a source task and use it to speed up learning in a related target task. We do so by learning a Mar ..."
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Cited by 8 (5 self)
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We propose the use of statistical relational learning, and in particular the formalism of Markov Logic Networks, for transfer in reinforcement learning. Our goal is to extract relational knowledge from a source task and use it to speed up learning in a related target task. We do so by learning a
Maxmargin weight learning for Markov logic networks
 In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD09). Bled
, 2009
"... Abstract. Markov logic networks (MLNs) are an expressive representation for statistical relational learning that generalizes both firstorder logic and graphical models. Existing discriminative weight learning methods for MLNs all try to learn weights that optimize the Conditional Log Likelihood (CL ..."
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Cited by 30 (5 self)
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Abstract. Markov logic networks (MLNs) are an expressive representation for statistical relational learning that generalizes both firstorder logic and graphical models. Existing discriminative weight learning methods for MLNs all try to learn weights that optimize the Conditional Log Likelihood
Bottomup learning of Markov logic network structure
 In Proceedings of the TwentyFourth International Conference on Machine Learning
, 2007
"... Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes firstorder logic and Markov networks. The current stateoftheart algorithm for learning MLN structure follows a topdown paradigm where many potential candidate structures a ..."
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Cited by 68 (7 self)
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Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes firstorder logic and Markov networks. The current stateoftheart algorithm for learning MLN structure follows a topdown paradigm where many potential candidate structures
Mapping and revising markov logic networks for transfer learning
 In Proceedings of the 22 nd National Conference on Artificial Intelligence (AAAI
, 2007
"... Transfer learning addresses the problem of how to leverage knowledge acquired in a source domain to improve the accuracy and speed of learning in a related target domain. This paper considers transfer learning with Markov logic networks (MLNs), a powerful formalism for learning in relational domains ..."
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Cited by 53 (5 self)
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Transfer learning addresses the problem of how to leverage knowledge acquired in a source domain to improve the accuracy and speed of learning in a related target domain. This paper considers transfer learning with Markov logic networks (MLNs), a powerful formalism for learning in relational
Results 11  20
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16,693