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DTSTART;VALUE=DATE:20240220
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SUMMARY:AAAI 2024
DESCRIPTION:38th Annual AAAI Conference on Artificial Intelligence\nThe purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers\, practitioners\, scientists\, students\, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations\, special tracks\, invited speakers\, workshops\, tutorials\, poster sessions\, senior member presentations\, competitions\, and exhibit programs\, and a range of other activities to be announced. \nWe expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this. \n\n \n\n\n\n\n\n\nDriven by its disciplinary diversity\, AAAI has incubated numerous AI sub-disciplines and conferences and has nurtured for decades the cohesion of AI. New communities often emerge when two or more disciplines come together in order to explore new opportunities and perspectives; today\, both are plentiful. The purpose of this year’s Bridge Program is to tap into new sources of innovation by cultivating collaboration between two or more communities directed towards a common goal. Our interpretation of bridges is broad and encompasses disciplines within and outside of AI. Hence\, the communities that our Bridge Program is intended to bring together could be distinct subfields of AI\, such as planning and learning\, or different disciplines that contribute to and benefit from AI\, such as AI and the humanities.
URL:https://www.neuropac.info/event/aaai-2024/
LOCATION:Vancouver\, Canada\, Vancouver\, Canada
CATEGORIES:Conference
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DTSTART;TZID=Europe/Zurich:20240227T180000
DTEND;TZID=Europe/Zurich:20240227T193000
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CREATED:20240105T080743Z
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SUMMARY:Aaron Spieler @ ONM - The ELM Neuron: An Efficient and Expressive Cortical Neuron Model Can Solve Long-Horizon Tasks
DESCRIPTION:From the Open Neuromorphic website. \nBiological cortical neurons are remarkably sophisticated computational devices\, temporally integrating their vast synaptic input over an intricate dendritic tree\, subject to complex\, nonlinearly interacting internal biological processes. \nWith the aim to explore the computational implications of leaky memory units and nonlinear dendritic processing\, we introduce the Expressive Leaky Memory (ELM) neuron model\, a biologically inspired phenomenological model of a cortical neuron. Remarkably\, by exploiting a few such slowly decaying memory-like hidden states and two-layered nonlinear integration of synaptic input\, our ELM neuron can accurately match the aforementioned input-output relationship with under ten-thousand trainable parameters. \nWe evaluate the model on various tasks with demanding temporal structures\, including the Long Range Arena (LRA) datasets\, as well as a novel neuromorphic dataset based on the Spiking Heidelberg Digits dataset (SHD-Adding). The ELM neuron reliably outperforms the classic Transformer or Chrono-LSTM architectures on these tasks\, even solving the Pathfinder-X task with over 70% accuracy (16k context length). \n\n\n\n\n\nAbout the Speaker\nAaron Spieler is a computational neuroscientist passionate about exploring the intersection of deep learning and neuroscience. After earning his Bachelor’s in Computer Science from the University of Potsdam\, he undertook an extended internship at Amazon Web Services working in deep learning based forecasting\, before further specializing with a Master’s in Computational Neuroscience at the University of Tübingen. Throughout his Master’s thesis and a subsequent internship at the Max Planck Institute for Intelligent Systems\, Aaron focused on phenomenological neuron modeling with applications to long-range prediction tasks. Pursuing this work allowed him to collaborate with excellent researchers from diverse backgrounds\, including Prof. Bernhard Schölkopf and Prof. Anna Levina.
URL:https://www.neuropac.info/event/aaron-spieler-onm-the-elm-neuron-an-efficient-and-expressive-cortical-neuron-model-can-solve-long-horizon-tasks/
LOCATION:Online
CATEGORIES:Talk,Workshop
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