Flash Forward is a show about possible (and not so possible) future scenarios. What would the warranty on a sex robot look like? How would diplomacy work if we couldn’t lie? Could there ever be a fecal transplant black market? (Complicated, it wouldn’t, and yes, respectively, in case you’re curious.) Hosted and produced by award winning science journalist Rose Eveleth, each episode combines audio drama and journalism to go deep on potential tomorrows, and uncovers what those futures might re ...
…
continue reading
Contenido proporcionado por The Thesis Review and Sean Welleck. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente The Thesis Review and Sean Welleck o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.
Player FM : aplicación de podcast
¡Desconecta con la aplicación Player FM !
¡Desconecta con la aplicación Player FM !
[04] Sebastian Nowozin - Learning with Structured Data: Applications to Computer Vision
MP3•Episodio en casa
Manage episode 302418441 series 2982803
Contenido proporcionado por The Thesis Review and Sean Welleck. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente The Thesis Review and Sean Welleck o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.
Sebastian Nowozin is currently a Researcher at Microsoft Research Cambridge. His research focuses on probabilistic deep learning, consequences of model misspecification, understanding agent complexity in order to improve learning efficiency, and designing models for reasoning and planning. His PhD thesis is titled "Learning with Structured Data: Applications to Computer Vision", which he completed in 2009. We discuss the work in his thesis on structured inputs and structured outputs, which involves beautiful ideas from polyhedral combinatorics and optimization. We talk about his recent work on Bayesian deep learning and the connections it has to ideas that he explored during his PhD. Episode notes: https://cs.nyu.edu/~welleck/episode4.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html
…
continue reading
47 episodios
MP3•Episodio en casa
Manage episode 302418441 series 2982803
Contenido proporcionado por The Thesis Review and Sean Welleck. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente The Thesis Review and Sean Welleck o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.
Sebastian Nowozin is currently a Researcher at Microsoft Research Cambridge. His research focuses on probabilistic deep learning, consequences of model misspecification, understanding agent complexity in order to improve learning efficiency, and designing models for reasoning and planning. His PhD thesis is titled "Learning with Structured Data: Applications to Computer Vision", which he completed in 2009. We discuss the work in his thesis on structured inputs and structured outputs, which involves beautiful ideas from polyhedral combinatorics and optimization. We talk about his recent work on Bayesian deep learning and the connections it has to ideas that he explored during his PhD. Episode notes: https://cs.nyu.edu/~welleck/episode4.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html
…
continue reading
47 episodios
همه قسمت ها
×Bienvenido a Player FM!
Player FM está escaneando la web en busca de podcasts de alta calidad para que los disfrutes en este momento. Es la mejor aplicación de podcast y funciona en Android, iPhone y la web. Regístrate para sincronizar suscripciones a través de dispositivos.