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    <title language="eng">New Ramsey multiplicity bounds and search heuristics</title>
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    <author>Olaf Parczyk</author>
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    <title language="eng">Categorification of Flag Algebras</title>
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    <title language="eng">The Four-Color Ramsey Multiplicity of Triangles</title>
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    <title language="eng">Extending the Continuum of Six-Colorings</title>
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    <title language="eng">Extending the Continuum of Six-Colorings</title>
    <parentTitle language="eng">Discrete Mathematics Days 2024</parentTitle>
    <identifier type="doi">10.37536/TYSP5643</identifier>
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    <author>Konrad Mundinger</author>
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    <author>Christoph Spiegel</author>
    <author>Max Zimmer</author>
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    <title language="eng">Sparse Model Soups</title>
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    <author>Christoph Spiegel</author>
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    <title language="eng">Compression-aware training of neural networks using Frank-Wolfe</title>
    <parentTitle language="eng">Mathematical Optimization for Machine Learning: Proceedings of the MATH+ Thematic Einstein Semester 2023</parentTitle>
    <identifier type="doi">10.1515/9783111376776-010</identifier>
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    <author>Max Zimmer</author>
    <submitter>Christoph Spiegel</submitter>
    <editor>K. Fackeldey</editor>
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    <author>Sebastian Pokutta</author>
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    <title language="eng">Deep Neural Network Training with Frank-Wolfe</title>
    <abstract language="eng">This paper studies the empirical efficacy and benefits of using projection-free first-order methods in the form of Conditional Gradients, a.k.a. Frank-Wolfe methods, for training Neural Networks with constrained parameters. We draw comparisons both to current state-of-the-art stochastic Gradient Descent methods as well as across different variants of stochastic Conditional Gradients. In particular, we show the general feasibility of training Neural Networks whose parameters are constrained by a convex feasible region using Frank-Wolfe algorithms and compare different stochastic variants. We then show that, by choosing an appropriate region, one can achieve performance exceeding that of unconstrained stochastic Gradient Descent and matching state-of-the-art results relying on L2-regularization. Lastly, we also demonstrate that, besides impacting performance, the particular choice of constraints can have a drastic impact on the learned representations.</abstract>
    <identifier type="arxiv">2010.07243</identifier>
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    <author>Sebastian Pokutta</author>
    <submitter>Franziska Schlösser</submitter>
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    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
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    <collection role="persons" number="pokutta">Pokutta, Sebastian</collection>
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    <collection role="persons" number="spiegel">Spiegel, Christoph</collection>
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