FrontPage / Learning Markov Logic

概要

Statistical Relational Learning (SRL) の代表的な枠組みのひとつである Markov Logic Networks [Richardson & Domingos 2006] とその周辺技術を理解し、SRL に関する研究動向を把握する。

日時
毎週水曜日 17:00-(ご飯を食べながら)
参加者
乾,渡邉,水野,井之上,山本,岡崎

方法

  • Pedro Domingos 氏の講義資料(http://homes.cs.washington.edu/~pedrod/803/)を参考に進める
  • 各回の説明担当者を決めておき、講義資料またはオリジナルの説明資料を用いて発表してもらう
    • 担当者は事前に参考文献を読み、ひととおり説明できるようにしておく
  • ご飯(ピザ、カレーほか)を食べながら勉強する

参考文献

Pedro Domingos and Daniel Lowd,
Markov Logic: An Interface Layer for AI, Morgan & Claypool, 2008.

担当

日付担当者講義タイトル参考文献の該当箇所
5/22井之上Introduction (🔒内部資料)Chapter 1
5/22井之上Markov networks (🔒内部資料)Section 2.2
6/5山本First-order logic and inductive logic programming (🔒内部資料)Section 2.1
6/12井之上Markov logic and other SRL approaches (🔒内部資料)Sections 2.3 and 2.4
6/26井之上Markov logic (contd.) (🔒内部資料)Sections 2.3 and 2.4
7/10水野InferenceChapter 3
7/17水野Inference (Lifted inference) (🔒内部資料)Chapter 3
7/25 13:00-岡崎Weight learning 🔒内部資料Section 4.1
8月末?井之上Structure learningSections 4.2, 4.3 and 4.4

参考リンク(ソフトウェア)

参考リンク(論文)

  • Application to discourse processing
    • Yufang Hou and Katja Markert and Michael Strube. Global Inference for Bridging Anaphora Resolution. NAACL-HLT2013. pdf
    • Islam Beltagy and Cuong Chau and Gemma Boleda and Dan Garrette and Katrin Erk and Raymond Mooney. Montague Meets Markov: Deep Semantics with Probabilistic Logical Form. *SEM2013. pdf
    • Katsumasa Yoshikawa, Masayuki Asahara, Yuji Matsumoto. Jointly Extracting Japanese Predicate-Argument Relation with Markov Logic. IJCNLP2011. pdf
    • James Blythe, Jerry R. Hobbs, Pedro Domingos, Rohit J. Kate and Raymond J. Mooney. Implementing Weighted Abduction in Markov Logic. IWCS2011. pdf
    • Dan Garrette, Katrin Erk, Raymond Mooney. Integrating Logical Representations with Probabilistic Information using Markov Logic. IWCS2011. pdf
    • Stefan Schoenmackers, Jesse Davis, Oren Etzioni and Daniel Weld. Learning First-Order Horn Clauses from Web Text. EMNLP2010. pdf
    • RohitJ. Kate RaymondJ. Mooney. Probabilistic Abduction using Markov Logic Networks. IJCAI 2009 on PAIR 2009. pdf
    • Stefan Schoenmackers, Oren Etzioni and Daniel Weld. Scaling Textual Inference to the Web. EMNLP2010. pdf
    • Hoifung Poon, Pedro Domingos. Joint Unsupervised Coreference Resolution with Markov Logic. EMNLP2008. pdf
  • Inference
    • Cutting Plane Aggregation: Jan Noessner, Mathias Niepert, Heiner Stuckenschmidt. RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models. AAAI2013. pdf
    • Lifted inference: Vibhav Gogate and Pedro Domingos. Probabilistic Theorem Proving. UAI2011. pdf
    • Cutting Plane Inference: Sebastian Riedel, Cutting Plane MAP Inference for Markov Logic. SRL 2009. pdf
    • MC-SAT: Hoifung Poon, Pedro Domingos. Sound and Efficient Inference with Probabilistic and Deterministic Dependencies. AAAI 2006. pdf
    • LazySAT: Parag Singla, Pedro Domingos. Memory-Efficient Inference in Relational Domains. AAAI 2006. pdf
    • SampleSAT: Wei Wei, Jordan Erenrich, and Bart Selman. Towards Efficient Sampling: Exploiting Random Walk Strategies. AAAI 2004. pdf
    • WalkSAT: Bart Selman, Henry Kautz, and Bram Cohen. Local Search Strategies for Satisfiability Testing. 1996. pdf
  • Structure/Weight Learning
    • Tuyen N. Huynh and Raymond J. Mooney. Max-Margin Weight Learning for Markov Logic Networks. SRL2009. pdf

Last-modified: 2020-04-10 (Fri) 08:14:15 (197d)
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