Unsupervised Learning, Clustering, and Self-Organization
Unsupervised learning does not require annotation or labeling from a human teacher; the idea is to learn the structure of the data from unlabeled examples. The most common unsupervised learning task is clustering, i.e. grouping instances into a discovered set of categories containing similar instances. Self-organizing maps in addition visualize the topology of the clusters on a map. Our work in this area includes applications on lexical semantics, topic modeling, and discovering latent class models, as well as methods for laterally connected, hierarchical, sequential-input, and growing self-organizing maps.
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Shruti Bhosale Formerly affiliated Masters Student shruti [at] cs utexas edu
Justine Blackmore Masters Alumni jblackmorehlista [at] yahoo com
Yoonsuck Choe Ph.D. Alumni choe [at] tamu edu
Craig Corcoran Ph.D. Student ccor [at] cs utexas edu
Daniel L. James Undergraduate Alumni
Elad Liebman Ph.D. Student eladlieb [at] cs utexas edu
Stephen Roller Ph.D. Student roller [at] cs utexas edu
Vito Ruiz Masters Alumni
Bryan Silverthorn Ph.D. Alumni bsilvert [at] cs utexas edu
Joseph Sirosh Ph.D. Alumni joseph sirosh [at] gmail com
Yiu Fai Sit Ph.D. Alumni yfsit [at] cs utexas edu
Austin Waters Ph.D. Alumni austin [at] cs utexas edu
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Infinite-Word Topic Models for Digital Media 2014
Austin Waters, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
A Mixture Model with Sharing for Lexical Semantics 2010
Joseph Reisinger and Raymond J. Mooney, In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2010), pp. 1173--1182, MIT, Massachusetts, USA, October 9--11 2010.
Cross-cutting Models of Distributional Lexical Semantics 2010
Joseph S. Reisinger, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Latent Class Models for Algorithm Portfolio Methods 2010
Bryan Silverthorn and Risto Miikkulainen, In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence 2010.
Spherical Topic Models 2010
Joseph Reisinger, Austin Waters, Bryan Silverthorn, and Raymond J. Mooney, In Proceedings of the 27th International Conference on Machine Learning (ICML 2010) 2010.
Spherical Topic Models 2009
Joseph Reisinger, Austin Waters, Bryan Silverthorn, and Raymond Mooney, In NIPS'09 workshop: Applications for Topic Models: Text and Beyond 2009.
Temporal Convolution Machines for Sequence Learning 2009
Alan J Lockett and Risto Miikkulainen, Technical Report AI-09-04, Department of Computer Sciences, the University of Texas at Austin.
Self-Organizing Distinctive State Abstraction Using Options 2007
Jefferson Provost, Benjamin J. Kuipers, and Risto Miikkulainen, In Proceedings of the 7th International Conference on Epigenetic Robotics 2007.
Self-Organization of Hierarchical Visual Maps with Feedback Connections 2006
Yiu Fai Sit and Risto Miikkulainen, Neurocomputing, Vol. 69 (2006), pp. 1309-1312.
Model-based Overlapping Clustering 2005
A. Banerjee, C. Krumpelman, S. Basu, Raymond J. Mooney and Joydeep Ghosh, In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-05) 2005.
Introduction: The Emerging Understanding of Lateral Interactions in the Cortex 1996
Risto Miikkulainen and Joseph Sirosh, In Lateral Interactions in the Cortex: Structure and Function, Sirosh, J., Miikkulainen, R., and Choe, Y. (Eds.) 1996. Electronic book, http://nn.cs.utexas.edu/web-pubs/htmlbook96.
Lateral Interactions In The Cortex: Structure And Function 1996
Joseph Sirosh, Risto Miikkulainen, and Yoonsuck Choe (editors), Electronic book, ISBN 0-9647060-0-8, http://nn.cs.utexas.edu/web-pubs/htmlbook96/. Austin, TX: The UTCS Neural Networks Research Group
Laterally Interconnected Self-Organizing Maps In Hand-Written Digit Recognition 1996
Yoonsuck Choe, Joseph Sirosh, and Risto Miikkulainen, In Advances in Neural Information Processing Systems 8, David S. Touretzky and Michael C. Mozer and Michael E. Hasselmo (Eds.), pp. 736-742 1996. Cambridge, MA: MIT Press.
Laterally Interconnected Self-Organizing Feature Map In Handwritten Digit Recognition 1995
Yoonsuck Choe, Masters Thesis, Department of Computer Sciences, The University of Texas at Austin. 65. Technical Report AI95-236.
SARDNET: A Self-Organizing Feature Map For Sequences 1995
Daniel L. James and Risto Miikkulainen, In Advances in Neural Information Processing Systems 7 (NIPS'94), G. Tesauro, D. S. Touretzky, and T. K. Leen (Eds.), pp. 577--584, Denver, CO 1995. Cambridge, MA: MIT Press.
Visualizing High-Dimensional Structure with the Incremental Grid Growing Network 1995
Justine Blackmore, Masters Thesis, Department of Computer Sciences, The University of Texas at Austin. Technical Report AI95-238.
Visualizing High-Dimensional Structure With The Incremental Grid Growing Neural Network 1995
Justine Blackmore and Risto Miikkulainen, In Machine Learning: Proceedings of the 12th Annual Conference, Armand Prieditis and Stuart Russell (Eds.), pp. 55-63, Austin, TX 1995. San Francisco, CA: Morgan Kaufmann. 55-63. Technical Repo...
How Lateral Interaction Develops In A Self-Organizing Feature Map 1993
Joseph Sirosh and Risto Miikkulainen, In Proceedings of the IEEE International Conference on Neural Networks (San Francisco, CA), pp. 1360-1365 1993. Piscataway, NJ: IEEE.
Incremental Grid Growing: Encoding High-Dimensional Structure Into A Two-Dimensional Feature Map 1993
Justine Blackmore and Risto Miikkulainen, In Proceedings of the IEEE International Conference on Neural Networks (San Francisco, CA), pp. 450-455 1993. Piscataway, NJ: IEEE.
Self-Organizing Process Based On Lateral Inhibition And Synaptic Resource Redistribution 1991
Risto Miikkulainen, In Proceedings of the 1991 International Conference on Artificial Neural Networks, Teuvo Kohonen and Kai M{"a}kisara and Olli Simula and Jari Kangas (Eds.), pp. 415-420 1991. Amsterdam: North-H...
Script Recognition With Hierarchical Feature Maps 1990
Risto Miikkulainen, Connection Science, Vol. 2 (1990), pp. 83-101.
SOFM The SOFM package contains C- and TK/TCL-code (integrated through SWIG) for the standard feature map algorithm for formi... 2002

LISSOM

The LISSOM package contains the C++, Python, and Scheme source code and examples for training and testing firing-rate...

2001

HFM The HFM package contains the C-code and data for training and testing the HFM memory organization and hierarchical class... 1994