{"id":1431,"date":"2019-02-28T11:05:00","date_gmt":"2019-02-28T02:05:00","guid":{"rendered":"http:\/\/cinetjp-static3.nict.go.jp\/english\/?p=1431"},"modified":"2022-08-27T21:30:34","modified_gmt":"2022-08-27T12:30:34","slug":"20190228_3279","status":"publish","type":"news","link":"http:\/\/cinetjp-static3.nict.go.jp\/english\/news\/20190228_3279\/","title":{"rendered":"Presentations and slides from the 5th CiNet Conf. partially available"},"content":{"rendered":"\n
Presentations:<\/strong> Shun-ichi Amari, RIKEN David Cox, MIT-IBM Watson AI Lab\/Harvard University<\/strong> Aapo Hyv\u00e4rinen, University College London\/University of Helsinki<\/strong> Odelia Schwartz, University of Miami<\/strong> Taro Toyoizumi, RIKEN\u00a0<\/strong> Kai Wang, NEC Corporation<\/strong> Slides:<\/strong><\/p>\n\n\n\n Ana Lu\u00edsa Pinho, Inria, CEA, Paris-Saclay University<\/strong>
(sound not picked in the first few minutes in some of the presentations)<\/p>\n\n\n\n
<\/strong>\u201cStatistical Neurodynamics of Deep Networks: Signal Propagation and Fisher Information\u201d<\/em><\/a><\/p>\n\n\n\n
\u201cPredictive Coding Models of Perception\u201d<\/em><\/a><\/p>\n\n\n\n
<\/strong>\u201cNonlinear independent component analysis: A principled framework for unsupervised learning\u201d<\/a><\/em><\/p>\n\n\n\n
\u201cImage statistics and cortical visual processing: V1, V2, and deep learning\u201d<\/a><\/em><\/p>\n\n\n\n
\u201cAn Optimization Approach to Understand Biological Search\u201d<\/a><\/em><\/p>\n\n\n\n
\u201cExperimental Platform for brain function model design\u201d<\/a><\/em><\/p>\n\n\n\n
\u201cIndividual Brain Charting, a high-resolution fMRI dataset for cognitive mapping of the human brain\u201d<\/a><\/em><\/p>\n\n\n\n