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proyectos:tfg:casos:redes_neuronales:start
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proyectos:tfg:casos:redes_neuronales:start [2018/01/19 15:12] Joaquín Herrero Pintado |
proyectos:tfg:casos:redes_neuronales:start [2018/05/26 15:53] Joaquín Herrero Pintado [Bibliografía a usar] |
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| - | ====== Causalidad y redes neuronales: Big Data ====== | + | ====== Causalidad y redes neuronales ====== |
| ===== Bibliografía a usar ===== | ===== Bibliografía a usar ===== | ||
| + | * [[proyectos:tfg:bibliografia:flack2017b]], apartado "Coarse-graining and compression in deep neural networks" | ||
| + | * [[proyectos:tfg:bibliografia:pearl2018]] | ||
| * [[proyectos:tfg:bibliografia:wheeler2015]] | * [[proyectos:tfg:bibliografia:wheeler2015]] | ||
| + | * [[https://blogs.kent.ac.uk/thereasoner/files/2015/01/TheReasoner-123.pdf|The Reasoner. Vol 12, num 3 – March 2018]], Entrevista a Sabina Leonelli. | ||
| + | https://twitter.com/ranilillanjum/status/977158403574390786 | ||
| + | |||
| + | @ranilillanjum: #ProBio18 @SabinaLeonelli https://t.co/XCOM7MS9Uc | ||
| ===== Otras fuentes ===== | ===== Otras fuentes ===== | ||
| Línea 18: | Línea 24: | ||
| --- [[https://stats.stackexchange.com/questions/4498/whats-the-relation-between-hierarchical-models-neural-networks-graphical-mode|What's the relation between hierarchical models, neural networks, graphical models, bayesian networks?]] | --- [[https://stats.stackexchange.com/questions/4498/whats-the-relation-between-hierarchical-models-neural-networks-graphical-mode|What's the relation between hierarchical models, neural networks, graphical models, bayesian networks?]] | ||
| - | The Epistemology of Data Use: Sabina Leonelli ALS, Dec. 1, 2017 \\ | + | **The Epistemology of Data Use: Sabina Leonelli ALS, Dec. 1, 2017** \\ |
| BSTRACT: This talk examines the epistemology of data by addressing the challenges raised by ‘big data science’, and particularly the dissemination and re-use of large datasets via intricate and nested infrastructures such as digital databases. Empirically, my analysis is grounded on the in-depth qualitative study of “data journeys”, that is ways in which datasets are circulated and used for a variety of purposes across several different contexts. Conceptually, the talk brings my previous work on the relational nature of data to bear on existing philosophy of inductive reasoning and the triangulation of multiple lines of evidence (most prominently by John Norton, Alison Wylie and William Wimsatt), with the aim of outlining conditions under which big data can be used to reliably inform inferential reasoning. I conclude by highlighting five ways in which data science that fails to operate under such conditions could significantly damage scientific methods and the credibility of research outputs. \\ | BSTRACT: This talk examines the epistemology of data by addressing the challenges raised by ‘big data science’, and particularly the dissemination and re-use of large datasets via intricate and nested infrastructures such as digital databases. Empirically, my analysis is grounded on the in-depth qualitative study of “data journeys”, that is ways in which datasets are circulated and used for a variety of purposes across several different contexts. Conceptually, the talk brings my previous work on the relational nature of data to bear on existing philosophy of inductive reasoning and the triangulation of multiple lines of evidence (most prominently by John Norton, Alison Wylie and William Wimsatt), with the aim of outlining conditions under which big data can be used to reliably inform inferential reasoning. I conclude by highlighting five ways in which data science that fails to operate under such conditions could significantly damage scientific methods and the credibility of research outputs. \\ | ||
| --- https://www.youtube.com/watch?v=qc1aQep4DE8 | --- https://www.youtube.com/watch?v=qc1aQep4DE8 | ||
proyectos/tfg/casos/redes_neuronales/start.txt · Última modificación: 2018/05/31 06:37 por Joaquín Herrero Pintado
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