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Modeling Individual Humans via a Secondary Task Transfer Learning Method

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Federated and Transfer Learning

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 27))

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Notes

  1. 1.

    https://www.kaggle.com/rohanrao/nifty50-stock-market-data?select=HDFC.csv.

References

  1. Almeida A, Azkune G (2018) Predicting human behaviour with recurrent neural networks. Appl Sci 8(2):305

    Article  Google Scholar 

  2. Banerjee A (2021) Combinets v2: improving conceptual expansion using sgd. In: 8th ACM IKDD CODS and 26th COMAD, pp 413–413

    Google Scholar 

  3. Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE 12(7):e0180944

    Article  Google Scholar 

  4. Boden MA (1998) Creativity and artificial intelligence. Artif Intell 103(1):347–356. Artificial Intelligence 40 years later

    Google Scholar 

  5. Chen J, Chen W, Huang C, Huang S, Chen A (2016) Financial time-series data analysis using deep convolutional neural networks. In: 2016 7th international conference on cloud computing and big data (CCBD), pp 87–92

    Google Scholar 

  6. Dai W, Chen Y, Xue G, Yang Q, Yu Y (2009) Translated learning: transfer learning across different feature spaces. In: Koller D, Schuurmans D, Bengio Y, Botto L (eds) Advances in neural information processing systems, vol 21. Curran Associates, Inc., pp 353–360

    Google Scholar 

  7. Das D, Massa H, Kulkarni A, Rekatsinas T (2020) An empirical analysis of the impact of data augmentation on knowledge distillation. arXiv:2006.03810

  8. Davis N, Hsiao C-P, Singh KY, Lin B, Magerko B (2017) Creative sense-making: Quantifying interaction dynamics in co-creation. In: Proceedings of the 2017 ACM SIGCHI conference on creativity and cognition, C&C ’17, New York, NY, USA. Association for Computing Machinery, pp 356–366

    Google Scholar 

  9. ElSaid A, Karnas J, Lyu Z, Krutz D, Ororbia AG, Desell T (2020) Neuro-evolutionary transfer learning through structural adaptation. In: International conference on the applications of evolutionary computation (Part of EvoStar). Springer, pp 610–625

    Google Scholar 

  10. ElSaid A, Karns J, Lyu Z, Krutz D, Ororbia A, Desell T (2020) Improving neuroevolutionary transfer learning of deep recurrent neural networks through network-aware adaptation. In: Proceedings of the 2020 genetic and evolutionary computation conference, pp 315–323

    Google Scholar 

  11. Fauconnier G (2001) Conceptual blending and analogy. The analogical mind: Perspectives from cognitive science, pp 255–286

    Google Scholar 

  12. Fei-Fei L, Fergus R, Perona P (2006) One-shot learning of object categories. IEEE Trans Pattern Anal Mach Intell 28(4):594–611

    Article  Google Scholar 

  13. Floreano D, Dürr P, Mattiussi C (2008) Neuroevolution: from architectures to learning. Evol Intel 1(1):47–62

    Article  Google Scholar 

  14. Gagné C, Shoben E (1997) Influence of thematic relations on the comprehension of modifier-noun combinations. J Exp Psychol Learn Mem Cogn 23:71–87

    Article  Google Scholar 

  15. Gavves E, Mensink T, Tommasi T, Snoek CGM, Tuytelaars T (2018) Active transfer learning with zero-shot priors: Reusing past datasets for future tasks. CoRR, abs/ arXiv:1510.01544

  16. Guzdial M, Liao N, Chen J, Chen S-Y, Shah S, Shah V, Reno J, Smith G, Riedl MO (2019) Friend, collaborator, student, manager: how design of an ai-driven game level editor affects creators. In: Proceedings of the 2019 CHI conference on human factors in computing systems, CHI ’19, New York, NY, USA. Association for Computing Machinery, pp 1-13

    Google Scholar 

  17. Guzdial M, Liao N, Riedl M (2018) Co-creative level design via machine learning. arXiv:1809.09420

  18. Guzdial M, Riedl MO (2018) Combinets: creativity via recombination of neural networks. arXiv:1802.03605

  19. Halevy A, Norvig P, Pereira F (2009) The unreasonable effectiveness of data. IEEE Intell Syst 24(2):8–12

    Article  Google Scholar 

  20. Jacob M, Magerko B (2018) Creative arcs in improvised human-computer embodied performances. In: Proceedings of the 13th international conference on the foundations of digital games, FDG ’18, New York, NY, USA, 2018. Association for Computing Machinery

    Google Scholar 

  21. Liapis A, Smith G, Shaker N (2016) Mixed-initiative content creation. In: Procedural content generation in games. Springer, pp 195–214

    Google Scholar 

  22. Liapis A, Yannakakis GN, Togelius J (2013) Sentient sketchbook: computer-assisted game level authoring

    Google Scholar 

  23. Ravi S, Larochelle H (2017) Optimization as a model for few-shot learning. In: ICLR

    Google Scholar 

  24. Suthamathi Saravanarajan V, Chen R-C, Dewi C, Chen LS (2020) Montecarlo approach for solving unbound knapsack problem. In: Proceedings of the 7th multidisciplinary in international social networks conference and the 3rd international conference on economics, management and technology, pp 1–5

    Google Scholar 

  25. Singamsetti M, Mahajan A, Guzdial M (2021) Conceptual expansion neural architecture search (cenas). arXiv:2110.03144

  26. Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell

    Google Scholar 

  27. Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3(1):1–40

    Google Scholar 

  28. Xian Y, Schiele B, Akata Z (2017) Zero-shot learning-the good, the bad and the ugly. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4582–4591

    Google Scholar 

  29. Yannakakis GN, Togelius J (2011) Experience-driven procedural content generation. IEEE Trans Affect Comput 2(3):147–161

    Article  Google Scholar 

  30. Zhang Y, Yang Q (2017) A survey on multi-task learning. arXiv:1707.08114

  31. Zuo H, Zhang G, Pedrycz W, Behbood V, Lu J (2017) Fuzzy regression transfer learning in takagi-sugeno fuzzy models. IEEE Trans Fuzzy Syst 25(6):1795–1807

    Article  Google Scholar 

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Acknowledgements

This work was funded by the Mathematics of Information Technology and Complex Systems (MITACS) Association and Servus Credit Union.

Author information

Authors and Affiliations

  1. Department of Computing Science, University of Alberta, 9119-116 St NW, Edmonton Alberta, T6G 2E8, Canada

    Anmol Mahajan & Matthew Guzdial

  2. Alberta Machine Intelligence Institute (Amii), 10065 Jasper Ave 1101, Edmonton, T5J 3B1, Canada

    Anmol Mahajan & Matthew Guzdial

Authors
  1. Anmol Mahajan
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  2. Matthew Guzdial
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Corresponding author

Correspondence to Anmol Mahajan .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada

    Roozbeh Razavi-Far

  2. Department of Computer Science, Western University, London, ON, Canada

    Boyu Wang

  3. University of Alberta and the Alberta Machine Intelligence Institute (Amii), Edmonton, AB, Canada

    Matthew E. Taylor

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Kowloon, China

    Qiang Yang

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Mahajan, A., Guzdial, M. (2023). Modeling Individual Humans via a Secondary Task Transfer Learning Method. In: Razavi-Far, R., Wang, B., Taylor, M.E., Yang, Q. (eds) Federated and Transfer Learning. Adaptation, Learning, and Optimization, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-031-11748-0_11

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