Tensor-train low-rank structure enables tractable near-optimal Bayesian inference for high-dimensional MIMO detection and soft-decision decoding.
Tensor-train decomposition
3 Pith papers cite this work. Polarity classification is still indexing.
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Entropy-guided MPS rank allocation compresses multimodal FL updates up to 56.8× on heterogeneous edge devices while improving accuracy over uncompressed FedAvg and cutting data-to-convergence by up to 66×.
A student model trained on JPEG-compressed chest CT via attention-style distillation and a factorized projection head reaches AUROC within 3–5 percentage points of the uncompressed-teacher baseline on three datasets, while halving the projection-head parameters.
citing papers explorer
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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.
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Entropy-Guided Tensor Compression for Multimodal Federated Learning on Edge Devices
Entropy-guided MPS rank allocation compresses multimodal FL updates up to 56.8× on heterogeneous edge devices while improving accuracy over uncompressed FedAvg and cutting data-to-convergence by up to 66×.
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Learning from Compressed CT: Feature Attention Style Transfer and Structured Factorized Projections for Resource-Efficient Medical Image Analysis
A student model trained on JPEG-compressed chest CT via attention-style distillation and a factorized projection head reaches AUROC within 3–5 percentage points of the uncompressed-teacher baseline on three datasets, while halving the projection-head parameters.