IU Cognitive Computing Laboratory
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Lab Meeting Schedule: 

Theme: Novel Architectures for Learning Distributed Semantic Representations

Internal Codename: Semantic Architectures 'N Distributional Memory And Neurons? (SANDMAN)


Date
Topic/Readings
Feb 7
BEAGLE (Mike)
  • ​Jones, M. N., & Mewhort, D. J. K. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114, 1-37.
Feb 14
Sparse Binary Memory Systems (Mike)
  • Kanerva, P. (2009). Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors. Cognitive Computation, 1(2), 139-159.
  • Recchia, G. L., Sahlgren, M., Kanerva, P., & Jones, M. N. (2015). Encoding sequential information in vector space models of semantics: Comparing holographic reduced representation and random permutation.Computational Intelligence & Neuroscience.
  • An Introduction to Random Indexing  (Magnus Sahlgren)
  • https://www.slideshare.net/dileepajayakody/w-40109236​ 
Feb 21
Holographic Neural Networks (Mike)
  • Jones, M. N. (2007). Holographic Neural Networks. Poster Presented at Psychonomic Society Annual Meeting
  • Plate, T. A. (2003) Convolution-based memory models.Encyclopedia of Cognitive Science. 
  • Plate, T. A. (1995). Holographic Reduced Representations. IEEE Transactions on Neural Networks. 
  • Plate, T. (1992). Holographic Recurrent Networks. NIPS-5.​
  • Kelly, M. A., Blostein, D., & Mewhort, D. J. K. (2013). Encoding structure in holographic reduced representations. CJEP.  
Feb 28
Kiran Kumar: Exploring NYT article data to understand individual and stylistic changes over years
  • ​Chen, D., & Manning, C. (2014). A fast and accurate dependency parser using neural networks. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 740-750).
  • Chen, W. T., & Palmer, M. (2017). Unsupervised AMR-dependency parse alignment. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers (Vol. 1, pp. 558-567).
    Chicago
Mar 7
Brand new hybrid structure + processing account: 
  • Rotaru, A. S., Vigliocco, G., & Frank, S. L. (2018, February 9). Modelling the structure and dynamics of semantic processing. Retrieved from psyarxiv.com/d8auy
Mar 14
Spring Break
Mar 21
Brand new hybrid structure + processing account: 
  • Rotaru, A. S., Vigliocco, G., & Frank, S. L. (2018, February 9). Modelling the structure and dynamics of semantic processing. Retrieved from psyarxiv.com/d8auy
Mar 28
Mike @ CMU
Apr 4
Mike in DC 
Apr 11
Quantum Semantic Models (Erik)  
  • Bruza, P. D., & Cole, R. J. (2006). Quantum logic of semantic space: An exploratory investigation of context effects in practical reasoning. arXiv preprint quant-ph/0612178.
  • Bruza, P., Kitto, K., Nelson, D., & McEvoy, C. (2009). Is there something quantum-like about the human mental lexicon?. Journal of Mathematical Psychology, 53(5), 362-377.
Apr 18
​Elastic Weight Consolidation (Keiland) 
  • Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., ... & Hassabis, D. (2017). Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 114(13), 3521-3526.
  • French, R. M. (1999). Catastrophic forgetting in connectionist networks. Trends in cognitive sciences, 3(4), 128-135.
  • Hasselmo, M. E. (2017). Avoiding catastrophic forgetting. Trends in cognitive sciences, 21(6), 407-408.
    Chicago
Apr 25
Spiking Neural Networks (Prudhvi)
  • Stewart, T. C., Bekolay, T., & Eliasmith, C. (2011). Neural representations of compositional structures: Representing and manipulating vector spaces with spiking neurons. Connection Science, 23(2), 145-153. 
  • Vreeken, J. (2003). Spiking neural networks, an introduction.
May 2
Reservoir Computing (Jack)
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