6 Matching Annotations
  1. Jul 2021
  2. psyarxiv.com psyarxiv.com
    Choice-confirmation bias and gradual perseveration in human reinforcement learning
    1
    1. lucyparfitt16 08 Jul 2021

      Palminteri, S. (2021). Choice-confirmation bias and gradual perseveration in human reinforcement learning [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/dpqj6

      is:preprint lang:en computational model confirmation bias reinforcement learning repetition bias behavioral science psychology modeling bias
    Visit annotations in context

    Tags

    Annotators

    URL

    psyarxiv.com/dpqj6/
  3. Jun 2021
  4. prosecco.gforge.inria.fr prosecco.gforge.inria.fr
    CadeBlanchetJCS14.pdf
    1
    1. blipp 21 Jun 2021
      has been adapted to thecomputational model in [14]; it uses type annotations to help the proo
      computational model
    Visit annotations in context

    Tags

    Annotators

    URL

    prosecco.gforge.inria.fr/personal/bblanche/publications/CadeBlanchetJCS14.pdf
  5. Mar 2021
  6. www.nature.com www.nature.com
    Forecasting influenza-like illness trends in Cameroon using Google Search Data
    1
    1. zoe_ikeotuonye 25 Mar 2021

      Nsoesie, E. O., Oladeji, O., Abah, A. S. A., & Ndeffo-Mbah, M. L. (2021). Forecasting influenza-like illness trends in Cameroon using Google Search Data. Scientific Reports, 11(1), 6713. https://doi.org/10.1038/s41598-021-85987-9

      is:article lang:en computational science infectious disease influenza data Google research mortality pandemic disease infection digital data machine learning machine learning model
    Visit annotations in context

    Tags

    Annotators

    URL

    nature.com/articles/s41598-021-85987-9
  7. Jul 2020
  8. arxiv.org arxiv.org
    Fast and principled simulations of the SIR model on temporal networks
    1
    1. Danaeioak 31 Jul 2020

      Holme, P. (2020). Fast and principled simulations of the SIR model on temporal networks. ArXiv:2007.14386 [Physics, q-Bio]. http://arxiv.org/abs/2007.14386

      is:article lang:en epidemiology computational epidemiology Susceptible-Infectious-Recovered (SIR) model temporal networks simulation
    Visit annotations in context

    Tags

    Annotators

    URL

    arxiv.org/abs/2007.14386
  9. Jun 2020
  10. arxiv.org arxiv.org
    Clustering - What Both Theoreticians and Practitioners are Doing Wrong
    1
    1. ErikStuchly 25 Jun 2020

      Ben-David, S. (2018). Clustering—What Both Theoreticians and Practitioners are Doing Wrong. ArXiv:1805.08838 [Cs, Stat]. http://arxiv.org/abs/1805.08838

      is:article lang:en cluster clustering tool machine learning unsupervised learning theory practice algorithm parameter computational task optimization model selection
    Visit annotations in context

    Tags

    Annotators

    URL

    arxiv.org/abs/1805.08838
  11. www.researchgate.net www.researchgate.net
    (PDF) Modeling the Absence of Framing Effect in an Experience-based Covid-19 Disease Problem
    1
    1. edampf 22 Jun 2020

      Sharma, N., Uttrani, S., & Dutt, V. (2020, June 19). Modeling the Absence of Framing Effect in an Experience-based Covid-19 Disease Problem. 18th Annual Meeting of the International Conference on Cognitive Modelling. https://www.researchgate.net/publication/342313460_Modeling_the_Absence_of_Framing_Effect_in_an_Experience-based_Covid-19_Disease_Problem

      is:article lang:en COVID-19 modeling framing effect computational cognitive model gain loss Instance-based learning
    Visit annotations in context

    Tags

    Annotators

    URL

    researchgate.net/publication/342313460_Modeling_the_Absence_of_Framing_Effect_in_an_Experience-based_Covid-19_Disease_Problem