A Family of Kernelized Matrix Costs for Multiple-Output Mixture Neural Networks
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918156183797760 |
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| author | Hu, Bo Príncipe, José C. |
| author_facet | Hu, Bo Príncipe, José C. |
| contents | Pairwise distance-based costs are crucial for self-supervised and contrastive feature learning. Mixture Density Networks (MDNs) are a widely used approach for generative models and density approximation, using neural networks to produce multiple centers that define a Gaussian mixture. By combining MDNs with contrastive costs, this paper proposes data density approximation using four types of kernelized matrix costs in the Hilbert space: the scalar cost, the vector-matrix cost, the matrix-matrix cost (the trace of Schur complement), and the SVD cost (the nuclear norm), for learning multiple centers required to define a mixture density. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24076 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Family of Kernelized Matrix Costs for Multiple-Output Mixture Neural Networks Hu, Bo Príncipe, José C. Machine Learning Pairwise distance-based costs are crucial for self-supervised and contrastive feature learning. Mixture Density Networks (MDNs) are a widely used approach for generative models and density approximation, using neural networks to produce multiple centers that define a Gaussian mixture. By combining MDNs with contrastive costs, this paper proposes data density approximation using four types of kernelized matrix costs in the Hilbert space: the scalar cost, the vector-matrix cost, the matrix-matrix cost (the trace of Schur complement), and the SVD cost (the nuclear norm), for learning multiple centers required to define a mixture density. |
| title | A Family of Kernelized Matrix Costs for Multiple-Output Mixture Neural Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.24076 |