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Haim Sompolinsky
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Haim Sompolinsky
@HSompolinsky
@Harvard Professor of MCB & Physics and Director of Swartz Program in Theoretical Neuroscience; @HebrewU Professor of Physics and Neuroscience (Emeritus)
Joined July 2018
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  • user avatar
    Haim Sompolinsky
    @HSompolinsky
    Mar 25, 2021
    I am excited to announce a new work by an outstanding PhD student Ben Sorscher from Ganguli Lab that links the geometry of neural representations to one of most remarkable cognitive human abilities, learning new concepts from only few examples. biorxiv.org/content/10.110…
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    Haim Sompolinsky
    @HSompolinsky
    Oct 6, 2021
    Qianyi Li and I are excited to announce the publication of our paper on the Kernel Renormalization Theory of Deep Learning in Phys Rev X link.aps.org/doi/10.1103/Ph… . We derive an exact stat mech solution of deep learning by successive integration of weights
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    Haim Sompolinsky
    @HSompolinsky
    Mar 29, 2024
    the title of LeCun's slide says it all.
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    Haim Sompolinsky
    @HSompolinsky
    Oct 30, 2022
    Our work on manifold geometric theory underlying fast learning of novel concepts led by Ben Sorcher of the Ganguli Lab is out. pnas.org/doi/pdf/10.107… We apply our theory to object manifold representations in deep neural network (DNN) and in macaque IT cortex.
  • user avatar
    Haim Sompolinsky
    @HSompolinsky
    Mar 16, 2022
    #cosyne22 Open position for a phd or postdoc in theory at the interface of deep learning and neuroscience. If you have suitable background please see me at the meeting.
  • user avatar
    Haim Sompolinsky
    @HSompolinsky
    Jun 5, 2019
    1/ A new paper provides the first, theory-based measurement of the untangling of object manifolds by several ImageNet trained DCNNs, using ImageNet point-cloud manifolds and smoothly warped images. biorxiv.org/content/10.110…
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  • user avatar
    Haim Sompolinsky
    @HSompolinsky
    Jan 8, 2024
    My Harvard/Neuro 231, 2024 Edition begins soon. It explores contemporary brain theory spanning local neuronal circuits as well as deep neural networks. It examines the relationship between network structure, dynamics, and computation. 1/3
    24K
  • user avatar
    Haim Sompolinsky
    @HSompolinsky
    Oct 6, 2022
    Postdoc position in Sompolinsky Group: If you are interested in doing exciting postdoc research at Harvard at the forefront of computational neuroscience and the interface between natural and artificial intelligence, send application and 3 letters to [email protected]
  • user avatar
    Haim Sompolinsky
    @HSompolinsky
    Mar 29, 2024
    Thanks, Yann for the great inspiring talk at Harvard.
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    Haim Sompolinsky
    @HSompolinsky
    Dec 19, 2022
    I am excited to announce the publication of the wonderful paper of Julia Steinberg on Associative memory of structured knowledge in scientific reports nature.com/articles/s4159…. Most neural models of associative memory store structureless knowledge as simple random patterns in RNNs
    22K
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    Haim Sompolinsky
    @HSompolinsky
    Sep 18, 2023
    I am excited to announce a recent work by Yehonatan Avidan and Qianyi Li arxiv.org/abs/2309.04522 presenting an analytical theory for learning dynamics in infinitely wide neural network.
    arXiv logo
    arxiv.org
    Connecting NTK and NNGP: A Unified Theoretical Framework for Wide...
    Artificial neural networks have revolutionized machine learning in recent years, but a complete theoretical framework for their learning process is still lacking. Substantial advances were...
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    Haim Sompolinsky
    @HSompolinsky
    Dec 9, 2020
    I am excited to announce a recent work with Harvard student Qianyi Li deriving exact Stat Mech theory of Deep Linear Neural Networks following supervised learning, revealing exquisite richness of generalization properties and layerwise representations
    arXiv logo
    arxiv.org
    Statistical Mechanics of Deep Linear Neural Networks: The...
    The success of deep learning in many real-world tasks has triggered an intense effort to understand the power and limitations of deep learning in the training and generalization of complex tasks,...
  • user avatar
    Haim Sompolinsky
    @HSompolinsky
    Jul 16, 2018
    Most significant advance in stat mech of learning since Gardner. For the first time theory incorporates the rich structure of realistic data.
    user avatar
    SueYeon Chung
    @s_y_chung
    Jul 10, 2018
    A new theory to analyze neural manifolds in high-dimensional data. We link the geometry of neural manifolds to their classification capacity. Exciting implications on understanding deep networks and sensory systems! With Haim Sompolinsky and Dan Lee. go.aps.org/2tVHcYm
  • user avatar
    Haim Sompolinsky
    @HSompolinsky
    Feb 20, 2020
    The Swartz Program at Harvard University seeks applications for postdoctoral fellows in theoretical and computational neuroscience. Please send application material to [email protected] and come to see me at Cosyne 2020.
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