CT-Lite combines Feature Attention Style Transfer (FAST) and Structured Factorized Projections (SFP) with contrastive learning to reach AUROC within 5-7% of uncompressed baselines on compressed CT volumes across three datasets while using far fewer parameters.
Tensor-train decomposition,
2 Pith papers cite this work. Polarity classification is still indexing.
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Tensor-train low-rank structure enables tractable near-optimal Bayesian inference for high-dimensional MIMO detection and soft-decision decoding.
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Learning from Compressed CT: Feature Attention Style Transfer and Structured Factorized Projections for Resource-Efficient Medical Image Analysis
CT-Lite combines Feature Attention Style Transfer (FAST) and Structured Factorized Projections (SFP) with contrastive learning to reach AUROC within 5-7% of uncompressed baselines on compressed CT volumes across three datasets while using far fewer parameters.
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A Tensor-Train Framework for Bayesian Inference in High-Dimensional Systems: Applications to MIMO Detection and Channel Decoding
Tensor-train low-rank structure enables tractable near-optimal Bayesian inference for high-dimensional MIMO detection and soft-decision decoding.