Dictionary learning for fMRI via Fused Gromov-Wasserstein optimal transport with amortized neural approximation to handle varying individual brain geometries.
Fused gromov-wasserstein distance for structured objects.Algorithms, 13(9):212
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Fused Gromov-Wasserstein distances are extended with feature selection via Lasso/Ridge regularization or simplex-constrained weights, yielding theoretical bounds, metric properties, and an alternating minimization algorithm.
InfiGFusion introduces graph-on-logits distillation with an O(n log n) Gromov-Wasserstein approximation to fuse LLMs by modeling token co-activations, reporting gains over baselines on 11 benchmarks.
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Learning fMRI activations dictionaries across individual geometries via optimal transport
Dictionary learning for fMRI via Fused Gromov-Wasserstein optimal transport with amortized neural approximation to handle varying individual brain geometries.
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Fused Gromov-Wasserstein Distance with Feature Selection
Fused Gromov-Wasserstein distances are extended with feature selection via Lasso/Ridge regularization or simplex-constrained weights, yielding theoretical bounds, metric properties, and an alternating minimization algorithm.
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InfiGFusion: Graph-on-Logits Distillation via Efficient Gromov-Wasserstein for Model Fusion
InfiGFusion introduces graph-on-logits distillation with an O(n log n) Gromov-Wasserstein approximation to fuse LLMs by modeling token co-activations, reporting gains over baselines on 11 benchmarks.