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News/Learning Compositional Latent Structure with Vector Networks
arxiv
PublishedMay 28, 2026 at 4:00 AM
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Learning Compositional Latent Structure with Vector Networks

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arXiv:2605.28007v1 Announce Type: cross Abstract: Deep networks are powerful function approximators, but they typically store many different computations in shared weight matrices, making it difficult to selectively reuse or adapt parts of them when a familiar structure appears in novel combinations

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#machine-learning#artificial-intelligence#neural-networks#compositional-generalization

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Mentioned models
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  • 01
    Vector Network (VN)
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Read original ↗All from arxiv →
Tags
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#machine-learning#artificial-intelligence#neural-networks#compositional-generalization

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arxivRetrieval Augmented Generation Compositionality2harxivVeriEvolve: Evaluator-First Evolution for Tool-Using Agents2harxivTNODEV: Toolbox for Neural ODE Verification2harxivLearning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding2h
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