✨Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models
📝 Summary:
Compositional generalization requires neural representations to decompose linearly into orthogonal per-concept components. This Linear Representation Hypothesis is theoretically grounded and empirically supported in vision models.
🔹 Publication Date: Published on Feb 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.24264
• PDF: https://arxiv.org/pdf/2602.24264
• Github: https://github.com/oshapio/necessary-compositionality
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#CompositionalGeneralization #VisionModels #NeuralNetworks #MachineLearning #AIResearch
📝 Summary:
Compositional generalization requires neural representations to decompose linearly into orthogonal per-concept components. This Linear Representation Hypothesis is theoretically grounded and empirically supported in vision models.
🔹 Publication Date: Published on Feb 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.24264
• PDF: https://arxiv.org/pdf/2602.24264
• Github: https://github.com/oshapio/necessary-compositionality
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#CompositionalGeneralization #VisionModels #NeuralNetworks #MachineLearning #AIResearch