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Modeling Human Cognition with RNNs and Curriculum Learning, w/ Kanaka Rajan - #524

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Manage episode 304077966 series 2355587
内容由TWIML and Sam Charrington提供。所有播客内容(包括剧集、图形和播客描述)均由 TWIML and Sam Charrington 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Today we’re joined by Kanaka Rajan, an assistant professor at the Icahn School of Medicine at Mt Sinai. Kanaka, who is a recent recipient of the NSF Career Award, bridges the gap between the worlds of biology and artificial intelligence with her work in computer science. In our conversation, we explore how she builds “lego models” of the brain that mimic biological brain functions, then reverse engineers those models to answer the question “do these follow the same operating principles that the biological brain uses?”

We also discuss the relationship between memory and dynamically evolving system states, how close we are to understanding how memory actually works, how she uses RNNs for modeling these processes, and what training and data collection looks like. Finally, we touch on her use of curriculum learning (where the task you want a system to learn increases in complexity slowly), and of course, we look ahead at future directions for Kanaka’s research.

The complete show notes for this episode can be found at twimlai.com/go/524.

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Manage episode 304077966 series 2355587
内容由TWIML and Sam Charrington提供。所有播客内容(包括剧集、图形和播客描述)均由 TWIML and Sam Charrington 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

Today we’re joined by Kanaka Rajan, an assistant professor at the Icahn School of Medicine at Mt Sinai. Kanaka, who is a recent recipient of the NSF Career Award, bridges the gap between the worlds of biology and artificial intelligence with her work in computer science. In our conversation, we explore how she builds “lego models” of the brain that mimic biological brain functions, then reverse engineers those models to answer the question “do these follow the same operating principles that the biological brain uses?”

We also discuss the relationship between memory and dynamically evolving system states, how close we are to understanding how memory actually works, how she uses RNNs for modeling these processes, and what training and data collection looks like. Finally, we touch on her use of curriculum learning (where the task you want a system to learn increases in complexity slowly), and of course, we look ahead at future directions for Kanaka’s research.

The complete show notes for this episode can be found at twimlai.com/go/524.

  continue reading

696集单集

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