Calendar of Events
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Lenka Zdeborova (Seminar Speaker)
Lenka Zdeborova (Seminar Speaker)
Title: Phase transition in learning with neural networks Abstract: Statistical physics has studied exactly solvable models of neural networks for more than four decades. In this talk, we will put this line of work in perspective of recent questions stemming from deep learning. We will describe several types of phase transition that appear in the…
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Postponed Continual Learning Working Group
Postponed Continual Learning Working Group
Weekly Meeting Group Discussion: Lifelong and Human-like Learning in Foundation Models Speaker: Mengye Ren (New York University) Assistant Professor Department of Computer Science Courant Institute of Mathematical Sciences Center for Data Science (joint) New York University Abstract: Real-world agents, including humans, learn from online, lifelong experiences. However, today’s foundation models primarily acquire knowledge through offline, iid…
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CTN: Adam Hantman
CTN: Adam Hantman
Title: Neural basis for skilled movements Abstract: Generating behavior is an incredible achievement of the nervous system, considering the range of possible actions and the complexity of musculoskeletal arrangements. Motor control involves understanding the surrounding environment, selecting appropriate plans, converting those plans into motor commands, and adaptively reacting to feedback. This seminar will review efforts…
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Multi-resource-cost Optimization for Neural Networks Models Working Group (NNMS)
Multi-resource-cost Optimization for Neural Networks Models Working Group (NNMS)
Title: Scope of the working group, example project, and literature Short Description: From Nikolaus Kriegeskorte's (Professor of Psychology and of Neuroscience (in the Mortimer B. Zuckerman Mind Brain Behavior Institute) lab, Eivinas Butkus (grad student) will show an example of a modeling project optimizing energetic demands along with accuracy in a vision task, and Josh…
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CTN: Wei Ji Ma
CTN: Wei Ji Ma
Title: Efficient coding in reward neurons Abstract: Two of the greatest triumphs of computational neuroscience have been efficient coding accounts of tuning properties of sensory neurons and reinforcement learning accounts of dopaminergic neurons in the midbrain. At first glance, these theories seem to have no connection, but I will argue that they do. One can…
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CTN: Quentin Huys (Seminar Speaker)
CTN: Quentin Huys (Seminar Speaker)
Title: Translating computational mechanisms to clinical applications Computational psychiatry is a rapidly growing field attempting to translate advances in computational neuroscience and machine learning into improved outcomes for patients suffering from mental illness. In this lecture, I will provide an overview over recent approaches for translating computational research into an understanding of symptoms, and mechanisms…
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CTN: Guillaume Hennequin
CTN: Guillaume Hennequin
Title: A recurrent network model of planning explains hippocampal replay and human behaviour Abstract: When faced with a novel situation, humans often spend substantial periods of time contemplating possible futures. For such planning to be rational, the benefits to behaviour must compensate for the time spent thinking. I will show how we recently captured these features…