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 hypernet- works for neural architecture search
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
DNG-Encoder represents NN weights as dynamic graphs to preserve sequential inference and powers INR2JLS, which raises INR classification accuracy by ~10% on CIFAR-100-INR.
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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Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space
DNG-Encoder represents NN weights as dynamic graphs to preserve sequential inference and powers INR2JLS, which raises INR classification accuracy by ~10% on CIFAR-100-INR.
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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.