A spectral vision transformer achieves equitable or superior performance with fewer parameters than standard ViTs, CNNs, and other models by using spectral projections for tokenization in limited-data medical imaging.
Symmetric gauge functions and unitarily invariant norms.The Quarterly Journal of Mathematics, 11(1):50–59
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IO-SVD performs SVD-based LLM compression by constructing a KL-aware double-sided whitening space and using first-order loss estimates for heterogeneous rank allocation.
Development of a shared-memory parallel high-performance implementation of star-M SVD for optimal compression of large scientific datasets.
citing papers explorer
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Spectral Vision Transformer for Efficient Tokenization with Limited Data
A spectral vision transformer achieves equitable or superior performance with fewer parameters than standard ViTs, CNNs, and other models by using spectral projections for tokenization in limited-data medical imaging.
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IO-SVD: Input-Output Whitened SVD for Adaptive-Rank LLM Compression
IO-SVD performs SVD-based LLM compression by constructing a KL-aware double-sided whitening space and using first-order loss estimates for heterogeneous rank allocation.
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High-Performance Star-M SVD for Big Data Compression
Development of a shared-memory parallel high-performance implementation of star-M SVD for optimal compression of large scientific datasets.