DIPA learns preconditioning operators via distillation from a teacher with a better sensing matrix to improve reconstruction quality for the student's physically constrained matrix in imaging inverse problems.
Gradient-based learning applied to document recognition
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Proposes FedACnnL for analytic layer-wise DNN training in federated settings and pFedACnnL for analytic personalized meta-learning, claiming 83-99% training time reduction and 4-8% accuracy gains over baselines with SOTA results in most tested cases.
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DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems
DIPA learns preconditioning operators via distillation from a teacher with a better sensing matrix to improve reconstruction quality for the student's physically constrained matrix in imaging inverse problems.
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Analytic Personalized Federated Meta-Learning
Proposes FedACnnL for analytic layer-wise DNN training in federated settings and pFedACnnL for analytic personalized meta-learning, claiming 83-99% training time reduction and 4-8% accuracy gains over baselines with SOTA results in most tested cases.