CS 598 Integrative Intelligent Information Systems

Instructors: D.A. Forsyth, J. Hockenmaier, CX. Zhai

Time: Tue, Thur 11h00-12h15, 3403 Siebel

CS 598 Integrative Intelligent Information Systems Final Project Presentations:

• In room 3403, Siebel Hall, Friday, May 9 from 1:30-4:30pm.

Notes

• Vision
  • Notes from DAF's first vision lecture
  • Notes from DAF's second vision lecture
• Language
  • Notes from JCH's first language lecture
• Information retrieval
  • Slides (in ppt) from CXZ's lectures on information retrieval
• Sampling
  • Notes (scanned in pdf) from DAF's remarks on sampling

Resources

• General
  • Proposal to NSF, Forsyth+Hockenmaier.
  • Proposal to NSF, Forsyth, Chang, Han, Hockenmaier, Roth, Zhai
• Words and Pictures
  • Words and Pictures: Categories, Modifiers, Depiction and Iconography, D.A. Forsyth, T. Berg, C. Alm, A. Farhadi, J. Hockenmaier, N. Loeff, G. Wang, Draft book chapter
• Background papers for Information Retrieval
  • Amit Singhal, Modern Information Retrieval: A Brief Overview. In IEEE Data Engineering Bulletin 24(4), pages 35-43, 2001.
    
  • ChengXiang Zhai, John Lafferty, A study of smoothing methods for language models applied to information retrieval, ACM Transactions on Information Systems ( ACM TOIS ), Vol. 22, No. 2, April 2004, pages 179-214.
    
  • ChengXiang Zhai and John Lafferty, A risk minimization framework for information retrieval , Information Processing and Management ( IP &M ), 42(1), Jan. 2006. pages 31-55.
    
    

Papers

• Collaborative Prediction
  1. Fast Maximum Margin Matrix Factorization for Collaborative Prediction, Jason D. M. Rennie, Nati Srebro, in Luc De Raedt, Stefan Wrobel (Eds.) Proceedings of the 22nd International Machine Learning Conference, ACM Press, 2005
  2. Scene Discovery by Matrix Factorization, N.Loeff, A. Farhadi and D.A. Forsyth, In review
  3. A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data, RK Ando, T Zhang - The Journal of Machine Learning Research, 2005
• Links, Hubs and Communities
  1. Authoritative sources in a hyperlinked environment, J. M. Kleinberg, Proc ACM, 1999.
  2. Core Algorithms in the CLEVER System, R. Kumar, P. Raghavan, S. Rajagopalan and A. Tomkins, ACM Transactions on Internet Technology, Vol. 6, No. 2, May 2006, Pages 131–152.
  3. Stable algorithms for link analysis, AY Ng, AX Zheng, MI Jordan - Proceedings of the 24th annual international ACM SIGIR 2001
• Words and pictures
  1. Discovering Objects and Their Location in Images J. Sivic, B. Russell, A.A. Efros, A. Zisserman, and W.T. Freeman, International Conference on Computer Vision (ICCV 2005), October, 2005.   
  2. Kobus Barnard, Pinar Duygulu, Nando de Freitas, David Forsyth, David Blei, and Michael I. Jordan, "Matching Words and Pictures", Journal of Machine Learning Research , Vol 3, pp 1107-1135. 2003 
• Knowledge provenance
  1. 1. Huang, J., and Fox, M.S., (2004), "Uncertainty in Knowledge Provenance", Proceedings of the European Semantic Web Symposium, Springer Lecture Notes in Computer
     Science.
     
  2. Paulo Pinheiro da Silva, Deborah L. McGuinness and Rob McCool. Knowledge Provenance Infrastructure. IEEE Data Engineering Bulletin. Vol. 26 No. 4, pages 26-32,
     December 2003.
     
  3. Deborah L. McGuinness and Paulo Pinheiro da Silva. Explaining Answers from the Semantic Web: The Inference Web Approach. Journal of Web Semantics, Vol. 1 No. 4,
     October 2004, pages 397-413.
  4. Measuring Article Quality in Wikipedia: Models and Evaluation, by Meiqun Hu, Ee-Peng Lim, Aixin Sun, Hady W. Lauw, and Ba-Quy Vuong, ACM Conference on Information and Knowledge Management (CIKM'07), Nov 2007.
     
  5. Ba-Quy Vuong, Ee-Peng Lim, Aixin Sun, Minh-Tam Le, Hady W. Lauw and Kuiyu Chang. On Ranking Controversies in Wikipedia: Models
     and Evaluation, in Proceedings of WSDM 2008.
     
  6. B. Shaparenko, T. Joachims, Information Genealogy: Uncovering the Flow of Ideas in Non-Hyperlinked Document Databases, Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2007.
  7. Autonomously semantifying wikipedia, Fei Wu and Dan Weld, Conference on Information and Knowledge Management archive, 41-50, 2007
• Opinion and attribution
  1. Razvan Bunescu; Raymond Mooney (ACL 2007) Learning to Extract Relations from the Web using Minimal Supervision
     
  2. Soo-Min Kim; Eduard Hovy (EMNLP 2007) Crystal: Analyzing Predictive Opinions on the Web
     
  3. Marius Pasca; Dekang Lin; Jeffrey Bigham; Andrei Lifchits; Alpa Jain, Names and Similarities on the Web: Fact Extraction in the Fast Lane
  4. Andrea Esuli; Fabrizio Sebastiani PageRanking WordNet Synsets: An Application to Opinion Mining
     
• Scaling things up
  1. Thorsten Brants; Ashok C. Popat; Peng Xu; Franz J. Och; Jeffrey Dean Large Language Models in Machine Translation
     
  2. David Talbot; Miles Osborne Smoothed Bloom Filter Language Models: Tera-Scale LMs on the Cheap
• Partially supervised learning
  1. A. Blum and T. Mitchell, Combining labeled and unlabeled data with co-training Proceedings of the eleventh annual conference on Computational learning, 92 - 100, 1998
  2. Ming-Wei Chang; Lev Ratinov; Dan Roth, Guiding Semi-Supervision with Constraint-Driven Learning
• Multiple sources of knowledge
  1. Trevor Cohn; Mirella Lapata Machine Translation by Triangulation: Making Effective Use of Multi-Parallel Corpora
     
  2. Hua Wu; Haifeng Wang Pivot Language Approach for Phrase-Based Statistical Machine Translation
     
  3. Shankar Kumar; Franz J. Och; Wolfgang Macherey Improving Word Alignment with Bridge Languages
• Learning to rank
  • C.J.C. Burges, R. Ragno and Q.V. Le, Learning to Rank with Nonsmooth Cost Functions, NIPS 06 (LambdaRank)
  • C.J.C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender, Learning to Rank using Gradient Descent, ICML 05 (RankNet)
  • Y. Cao, J. Xu, T. Liu, H. Li, Y. Huang, H. Hon, Adapting ranking SVM to document retrieval, SIGIR 2006. (Ranking SVM)
• Non-parametric models
  • Mark Johnson, Thomas L. Griffiths and Sharon Goldwater Adaptor Grammars: A Framework for Specifying
    Compositional Nonparametric Bayesian Models, to appear in Proceedings of NIPS.
    
  • Unsupervised Coreference Resolution in a Nonparametric Bayesian Model, Aria Haghighi and Dan Klein, In proceedings of ACL 2007.