LARGO uses a low-rank hypernetwork with CP decomposition to unify 2^N-1 missing-modality models into one, ranking first in 47 of 52 configurations on BraTS and ISLES with small Dice gains over baselines.
Graph hypernetworks for neural architecture search
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FRAMP generates client-specific models from compact descriptors in federated learning, trains tailored submodels, and aligns representations to balance personalization with global consistency.
GRAM meta-graph search plus structure pruning yields SwiftNet models with 2.15x higher accuracy density than MobileNet-V2 and 26x lower search cost than FBNet on ImageNet edge constraints.
citing papers explorer
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LARGO: Low-Rank Hypernetwork for Handling Missing Modalities
LARGO uses a low-rank hypernetwork with CP decomposition to unify 2^N-1 missing-modality models into one, ranking first in 47 of 52 configurations on BraTS and ISLES with small Dice gains over baselines.
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Representation-Aligned Multi-Scale Personalization for Federated Learning
FRAMP generates client-specific models from compact descriptors in federated learning, trains tailored submodels, and aligns representations to balance personalization with global consistency.
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SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures
GRAM meta-graph search plus structure pruning yields SwiftNet models with 2.15x higher accuracy density than MobileNet-V2 and 26x lower search cost than FBNet on ImageNet edge constraints.