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X-ORIGINAL-URL:https://arni-institute.org
X-WR-CALDESC:Events for ARNI
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X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
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TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20240404T080000
DTEND;TZID=UTC:20240404T170000
DTSTAMP:20260513T181843
CREATED:20240315T190701Z
LAST-MODIFIED:20240315T190701Z
UID:649-1712217600-1712250000@arni-institute.org
SUMMARY:Data Science Day 2024
DESCRIPTION:“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.”\nIf you are interested in the event please register on their event page.
URL:https://arni-institute.org/event/data-science-day-2024/
LOCATION:Alfred Lerner Hall\, 2920 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240404T133000
DTEND;TZID=UTC:20240404T144000
DTSTAMP:20260513T181843
CREATED:20240326T190451Z
LAST-MODIFIED:20240401T223135Z
UID:740-1712237400-1712241600@arni-institute.org
SUMMARY:Continual Learning Working Group
DESCRIPTION:Weekly Meeting Group Discussion: \nPaper Topic: https://arxiv.org/abs/2309.10105 \nZoom: https://columbiauniversity.zoom.us/j/94783759415?pwd=cTlDTDdCVk9vdEV0QzRKL0hKQW1Kdz09
URL:https://arni-institute.org/event/continual-learning-working-group-6/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240405T113000
DTEND;TZID=UTC:20240405T130000
DTSTAMP:20260513T181843
CREATED:20240402T000158Z
LAST-MODIFIED:20240404T004652Z
UID:760-1712316600-1712322000@arni-institute.org
SUMMARY:Misha Tsodyks
DESCRIPTION:Title: Putative synaptic theory of temporal order encoding in working memory\n(Joint work with Gianluigi Mongillo) \nAbstract: 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\, according to which stimuli are encoded in working memory by selective short-term facilitation of corresponding recurrent synaptic connections. We further suggest that if synapses exhibit longer-term forms of facilitation\, e.g. synaptic augmentation\, encodings acquire a ‘primacy gradient’\, i.e. stimuli presented earlier are stronger encoded compared to later presented ones. We propose a simple way the order information can be retrieved. The new model also sheds new light on the important issue of working memory capacity. We suggest that one should distinguish between retrieval capacity which is limited to very few items\, and representational capacity that can be significantly larger.
URL:https://arni-institute.org/event/misha-tsodyks/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240409T114000
DTEND;TZID=UTC:20240409T130000
DTSTAMP:20260513T181843
CREATED:20240408T194253Z
LAST-MODIFIED:20240408T194253Z
UID:777-1712662800-1712667600@arni-institute.org
SUMMARY:Automating Analysis in Biology Using AI\, From Data to Discovery
DESCRIPTION:Speaker: Markus Marks (Caltech)\n\n\nTitle: Automating Analysis in Biology Using AI\, From Data to Discovery\n\n\n\nTime and Place: Davis Auditorium\, 11:40am\, Tuesday April 9\n\n\n\nAbstract: 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 GPUs at lower costs and fast-improving AI algorithms to effectively handle the rapid influx of data and extract scientific insights with manageable human effort? My work focuses on integrating machine learning into biology and medicine with three core goals: reducing human effort in data annotation\, mitigating human bias in annotations\, and uncovering concealed patterns within biomedical data through data-driven approaches.\n\nThis talk will focus on tackling these challenges\, removing human effort and bias step-by-step. I will elucidate this approach with recent work on behavioral and cellular data analysis\, starting with the application of machine learning to quantify animal behavior automatically in neuroscience experiments. I will then present our recent efforts to develop foundational models for scientific applications\, showcased by a cellular segmentation model that generalizes across a wide range of cell types. Furthermore\, I will show how we can move beyond human-generated labels and discover features directly from the data using self-supervision and experimental observations. Finally\, I will outline how these technologies can be combined to accelerate analysis and facilitate discovery for scientific experiments.\n\nBio: Markus is a postdoc at Caltech working in the computer vision group with Pietro Perona. He received his Ph.D. at the Institute for Neuroinformatics at ETH Zurich. Currently\, Markus focuses on developing machine learning algorithms to enhance scientific discovery in biology and medicine\, collaborating closely with domain experts. Markus organized the interdisciplinary MABe workshop in 2023 with Jennifer Sun from Cornell and the Kennedy lab at Northwestern\, aiming to bring together people and perspectives from different fields working on interacting agents.
URL:https://arni-institute.org/event/automating-analysis-in-biology-using-ai-from-data-to-discovery/
LOCATION:Davis Auditorium\, 530 W 120th St\, New York\, NY 10027\, New York\, NY\, 10027
ORGANIZER;CN="Colloquium":MAILTO:https://lists.cs.columbia.edu/mailman/listinfo/colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240411T133000
DTEND;TZID=UTC:20240411T144000
DTSTAMP:20260513T181843
CREATED:20240401T223314Z
LAST-MODIFIED:20240410T190727Z
UID:755-1712842200-1712846400@arni-institute.org
SUMMARY:Continual Learning Working Group
DESCRIPTION:Weekly Meeting Group Discussion: \nPaper Topic: https://direct.mit.edu/neco/article/35/11/1797/117579/Reducing-Catastrophic-Forgetting-With-Associative \nZoom: https://columbiauniversity.zoom.us/j/94783759415?pwd=cTlDTDdCVk9vdEV0QzRKL0hKQW1Kdz09
URL:https://arni-institute.org/event/continual-learning-working-group-7/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240412T113000
DTEND;TZID=UTC:20240412T130000
DTSTAMP:20260513T181843
CREATED:20240410T180731Z
LAST-MODIFIED:20240410T180759Z
UID:787-1712921400-1712926800@arni-institute.org
SUMMARY:Adam Charles
DESCRIPTION:Title: Micron brain data at scale: computational challenges in imaging and analysis. \nAbstract: 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 I will discuss ongoing work in multi-photon data analysis. This work seeks to expand our capabilities to extract scientifically rich information from large-scale data of sub-micron targets that represent how circuits compute and how those computations adapt over time. Specifically\, I will discuss recent machine learning image enhancement for tracking synaptic strength in-vivo at scale\, and a morphology-independent image segmentation algorithm for identifying geometrically complex fluorescing objects (e.g.\, dendritic and wide-field imaging). Next I will discuss the analysis challenges if inferring meaningful representations of brain-wide activity provided by imaging advances. Specifically\, brain-wide data represents many parallel and distributed computations. I will discuss recent work building on the intuition of the “neural data manifold”\, and present a decomposed linear dynamical systems (dLDS) model that can capture the nonlinear and non-stationary properties of the neural trajectories along this manifold. dLDS learns a concise model of such dynamics by breaking up the system into several independent\, overlapping systems that are each interpretable as linear systems. I will demonstrate how this model finds meaningful trajectories both in synthetic data and in “whole-brain” C. elegans imaging.
URL:https://arni-institute.org/event/adam-charles/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240412T150000
DTEND;TZID=UTC:20240412T170000
DTSTAMP:20260513T181843
CREATED:20240319T000436Z
LAST-MODIFIED:20240409T195851Z
UID:673-1712934000-1712941200@arni-institute.org
SUMMARY:Animal Behavior Video Analysis Working Group
DESCRIPTION:Title: Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning \nAbstract: 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 Drosophila melanogaster} in the \mujoco physics engine. Our model is general-purpose\, enabling the simulation of diverse fly behaviors\, both on land and in the air. We demonstrate the generality of our model by simulating realistic locomotion\, both flight and walking. To support these behaviors\, we have extended \mbox{MuJoCo} with phenomenological models of fluid forces and adhesion forces. Through data-driven end-to-end reinforcement learning\, we demonstrate that these advances enable the training of neural network controllers capable of realistic locomotion along complex trajectories based on high-level steering control signals. With a visually guided flight task\, we demonstrate a neural controller that can use the vision sensors of the body model to control and steer flight. Our project is an open-source platform for modeling neural control of sensorimotor behavior in an embodied context. \nJoin Zoom Meeting:\nhttps://columbiauniversity.zoom.us/j/98060956155?pwd=eVJDY0JOdWV4U1R4emt3dnNPbElWdz09  \nMeeting ID: 980 6095 6155\nPasscode: 263132
URL:https://arni-institute.org/event/animal-behavior-video-analysis-working-group-4/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240415T060000
DTEND;TZID=UTC:20240415T200000
DTSTAMP:20260513T181843
CREATED:20240411T000121Z
LAST-MODIFIED:20240416T223659Z
UID:795-1713160800-1713211200@arni-institute.org
SUMMARY:Breakthrough Technologies
DESCRIPTION: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 exciting glimpse of how these new technologies will enhance our future. (Invitation Only) \nThe lively panel will include: \n\nDario Gil\, SVP and Director of Research\, IBM \n\nDr. Gil leads innovation efforts at IBM\, directing research strategies in areas including AI\, cloud\, quantum computing\, and exploratory science. \n\nXaq Pitkow Associate Director\, ARNI \n\nDr. Pitkow is a computational neuroscientist who develops mathematical theories of the brain and general principles of intelligent systems. \n\nJeannette Wing\, EVPR and Professor of Computer Science\, Columbia University\n\nDr. Wing’s research contributes to trustworthy AI\, security and privacy\, specification and verification\, concurrent and distributed systems\, programming languages\, and software engineering.
URL:https://arni-institute.org/event/breakthrough-technologies/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240418T133000
DTEND;TZID=UTC:20240418T144000
DTSTAMP:20260513T181843
CREATED:20240403T195744Z
LAST-MODIFIED:20240403T195911Z
UID:767-1713447000-1713451200@arni-institute.org
SUMMARY:Continual Learning Working Group
DESCRIPTION:Weekly Meeting Group Discussion: Saket Navlakha\, Associate Professor at Cold Spring Harbor Labs (Available via Zoom) \nSaket 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 system that performs continual associative learning between odors and their associated valences. In the first layer\, inputs (odors) are encoded using sparse\, high-dimensional representations\, which reduces memory interference by activating nonoverlapping populations of neurons for different odors. In the second layer\, only the synapses between odor-activated neurons and the odor’s associated output neuron are modified during learning; the rest of the weights are frozen to prevent unrelated memories from being overwritten. The takeaway is that fruit flies evolved an efficient continual associative learning algorithm\, and circuit mechanisms from neuroscience can be translated to improve machine computation. \nZoom: https://columbiauniversity.zoom.us/j/94783759415?pwd=cTlDTDdCVk9vdEV0QzRKL0hKQW1Kdz09
URL:https://arni-institute.org/event/continual-learning-working-group-8/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240419T113000
DTEND;TZID=UTC:20240419T130000
DTSTAMP:20260513T181843
CREATED:20240410T234910Z
LAST-MODIFIED:20240416T184148Z
UID:792-1713526200-1713531600@arni-institute.org
SUMMARY:Shihab Shamma
DESCRIPTION:Title: The auditory cortex: A sensorimotor fulcrum for speech and music perception \nAbstract: 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 all these demands by segregating the sound sources and attending to a few\, and then directing them to be semantically decoded in the language or music areas of the brain. It also sends collateral signals to motor areas where the sounds could be produced and controlled (e.g.\, the vocal tract or hands). All these regions in turn reflect back to the auditory cortex their expectations and predictions of the activations due to the incoming sound streams.I shall review in this talk computational models of several such phenomena\, and discuss the experimental findings that test their underlying assumptions in humans and ferrets while they segregate speech mixtures\, imagine music\, or listen to songs.
URL:https://arni-institute.org/event/shihab-shamma/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240425T133000
DTEND;TZID=UTC:20240425T144000
DTSTAMP:20260513T181843
CREATED:20240416T185339Z
LAST-MODIFIED:20240416T185339Z
UID:804-1714051800-1714056000@arni-institute.org
SUMMARY:Continual Learning Working Group - Creative Group Brainstorming Session
DESCRIPTION:
URL:https://arni-institute.org/event/continual-learning-working-group-creative-group-brainstorming-session/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240426T113000
DTEND;TZID=UTC:20240426T130000
DTSTAMP:20260513T181843
CREATED:20240416T222623Z
LAST-MODIFIED:20240423T171216Z
UID:809-1714131000-1714136400@arni-institute.org
SUMMARY:Roberta Raileanu
DESCRIPTION:Title: Teaching Large Language Models to Reason with Reinforcement Learning \nAbstract: 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 sparse and dense rewards provided to the LLM both heuristically and via a learned reward model. However\, even with RL fine-tuning\, LLM reasoning remains imperfect. Prior work found that LLMs can further improve their reasoning via online refinements. However\, in our new work we show that LLMs struggle to identify when and where to refine their reasoning without access to external feedback. Outcome-based Reward Models (ORMs) trained to predict the correctness of the final answer\, can indicate when to refine. Process Based Reward Models (PRMs) trained to predict correctness of intermediate steps\, can indicate where to refine. But PRMs are expensive to train\, requiring extensive human annotations. We introduce Stepwise ORMs (SORMs) which are trained only on synthetic data\, to approximate the expected future reward of the optimal policy\, or V*.  Our experiments show that SORMs can more accurately detect incorrect reasoning steps compared to ORMs\, thus improving downstream accuracy on reasoning tasks. For the question of how to refine LLM reasoning\, we find that global and local refinements have complementary benefits\, so combining both of them achieves the best results. With this strategy we can improve the accuracy of a LLaMA-2 13B model (already fine-tuned with RL) on GSM8K from 53% to 65% when greedily sampled.
URL:https://arni-institute.org/event/roberta-raileanu/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240429T113000
DTEND;TZID=UTC:20240429T130000
DTSTAMP:20260513T181843
CREATED:20240416T222403Z
LAST-MODIFIED:20240423T191956Z
UID:806-1714390200-1714395600@arni-institute.org
SUMMARY:Lenka Zdeborova (Seminar Speaker)
DESCRIPTION:Title: Phase transition in learning with neural networks  \nAbstract: 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 high-dimensional limit as a function of the amount of data. Discontinuous phase transitions are linked to adjacent algorithmic hardness. This so-called hard phase influences the behaviour of gradient-descent-like algorithms. We show a case where the hardness is mitigated by overparametrization\, proposing that the benefits of overparametrization may be linked to the usage of a specific type of algorithm. We will also discuss recent progress in identifying phase transitions and their consequences in networks with attention layers and in sampling with generative diffusion-based networks.
URL:https://arni-institute.org/event/lenka-zdeborova-seminar-speaker/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
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