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block-term-decomposition

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Interpretable Block-Term Tensor Network (BTTN) for predicting future-onset (incident) atrial fibrillation from a single sinus-rhythm 12-lead ECG on MIMIC-IV-ECG: the glass-box (time x lead) factor parameters ARE the explanation, with a measured-faithfulness framework, at parity with a CNN. Patient-grouped CV, patient-bootstrap CIs.

  • Updated Jul 6, 2026
  • Python

Lead-field-constrained block-term decomposition of evoked EEG: solves the EEG inverse problem and decomposes the signal jointly, returning anatomically-grounded spatiotemporal components (a focal cortical source map, a time course, and a trial loading per block).

  • Updated Jul 4, 2026
  • Python

Block-Term Operator Theory: why block-term rank-(L,L,1) neural operators generalize better than CP / Tucker / TT at matched capacity, not by more expressivity but as a tighter inductive bias. A least-squares generalization separation Theta((RL - mu_band) K / n), a complete variance-ordering theorem across all four tensor formats, an adaptive for...

  • Updated Jul 18, 2026
  • Python

BT-FNO / MoBTE: parametrize the FNO spectral weight as a sum of R rank-(L,L,1) block-terms - the sum-of-Tucker-blocks that CP-FNO and Tucker-FNO bracket but neither covers - and route a mixture of them (MoBTE) so one foundation operator serves many PDEs. On real multi-PDE PDEBench at matched parameters the block-term kernel beats CP by 8-14% and...

  • Updated Jul 21, 2026
  • Python

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