Calendar of Events
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2 events,
Data Science Day 2024
Data Science Day 2024
"The Data Science Institute’s flagship annual event connects innovators in industry and government to Columbia researchers who are propelling advances across every sector with data science." If you are interested in the event please register on their event page.
Continual Learning Working Group
Continual Learning Working Group
Weekly Meeting Group Discussion: Paper Topic: https://arxiv.org/abs/2309.10105 Zoom: https://columbiauniversity.zoom.us/j/94783759415?pwd=cTlDTDdCVk9vdEV0QzRKL0hKQW1Kdz09
1 event,
Misha Tsodyks
Misha Tsodyks
Title: Putative synaptic theory of temporal order encoding in working memory (Joint work with Gianluigi Mongillo) Abstract: Overwhelming evidence indicates that working memory automatically encodes incoming stimuli in the correct presentation order. How this is achieved in the brain is however not well understood. We addressed this issue in the framework of our previously proposed synaptic theory,…
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Automating Analysis in Biology Using AI, From Data to Discovery
Automating Analysis in Biology Using AI, From Data to Discovery
Speaker: Markus Marks (Caltech) Title: Automating Analysis in Biology Using AI, From Data to Discovery Time and Place: Davis Auditorium, 11:40am, Tuesday April 9 Abstract: Thanks to improved sensors and decreasing data acquisition and storage costs, biologists are increasingly able to collect more and higher quality data. How can we harness the expanding capabilities of…
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Continual Learning Working Group
Continual Learning Working Group
Weekly Meeting Group Discussion: Paper Topic: https://direct.mit.edu/neco/article/35/11/1797/117579/Reducing-Catastrophic-Forgetting-With-Associative Zoom: https://columbiauniversity.zoom.us/j/94783759415?pwd=cTlDTDdCVk9vdEV0QzRKL0hKQW1Kdz09
2 events,
Adam Charles
Adam Charles
Title: Micron brain data at scale: computational challenges in imaging and analysis. Abstract: Uncovering the principles of neural computation requires 1) new methods to observe micron-level targets at scale and 2) interpretable models of high-dimensional time-series. In this talk I will cover recent advances in leveraging advanced data models based on latent sparsity and low-dimensionality to tackle key challenges in both domains. First…
Animal Behavior Video Analysis Working Group
Animal Behavior Video Analysis Working Group
Title: Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning Abstract: The body of an animal determines how the nervous system produces behavior. Therefore, detailed modeling of the neural control of sensorimotor behavior requires a detailed model of the body. Here we contribute an anatomically-detailed biomechanical whole-body model of the fruit fly {\em…
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Breakthrough Technologies
Breakthrough Technologies
Queens, NY – The New York Hall of Science (NYSCI), the AI Institute for Artificial and Natural Intelligence (ARNI), and the Fu Foundation School of Engineering and Applied Science at Columbia University will feature an engaging panel discussion exploring recent developments in quantum computing and AI. The goal of the discussion is to provide an…
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Continual Learning Working Group
Continual Learning Working Group
Weekly Meeting Group Discussion: Saket Navlakha, Associate Professor at Cold Spring Harbor Labs (Available via Zoom) Saket Navlakha, Associate Professor at Cold Spring Harbor Labs, will present his work, "Reducing Catastrophic Forgetting With Associative Learning: A Lesson From Fruit Flies". In this work, the authors identified a two-layer neural circuit in the fruit fly olfactory…
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Shihab Shamma
Shihab Shamma
Title: The auditory cortex: A sensorimotor fulcrum for speech and music perception Abstract: The Auditory cortex sits at the center of all auditory-motor tasks and percepts, from listening to our voice as we speak, to the music that we play, and to the complex sound mixtures that we seek to perceive. The auditory cortex orchestrates…
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Roberta Raileanu
Roberta Raileanu
Title: Teaching Large Language Models to Reason with Reinforcement Learning Abstract: In this talk, I will discuss how we can use Reinforcement Learning (RL) to improve reasoning in Large Language Models (LLM), as well as when, where, and how to refine LLM reasoning. First, we study how different RL-like algorithms can improve LLM reasoning. We investigate both…
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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…