SSA and DA extract barrier-sensitive mode separation from the autocovariance matrix of a unique constant-coefficient diffusion with the given density as stationary distribution.
Denoising diffusion probabilistic models
4 Pith papers cite this work. Polarity classification is still indexing.
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Germline-absorbing discrete diffusion uses the germline sequence as the absorbing state to reduce germline bias in antibody modeling, raising non-germline residue prediction accuracy from 26% to 46% and improving conditional generation tradeoffs over EvoProtGrad.
CollaFuse enables collaborative diffusion model training by splitting computation between resource-limited clients and a central server to reduce local burden and raw data sharing.
Standard 3D U-Net remains a strong baseline for multi-compartment cardiac segmentation, with handcrafted shape priors yielding at best marginal or negative effects on MM-WHS CT and WHS++ datasets.
citing papers explorer
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Measuring and Decomposing Mode Separation via the Canonical Diffusion
SSA and DA extract barrier-sensitive mode separation from the autocovariance matrix of a unique constant-coefficient diffusion with the given density as stationary distribution.
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Conditional generation of antibody sequences with classifier-guided germline-absorbing discrete diffusion
Germline-absorbing discrete diffusion uses the germline sequence as the absorbing state to reduce germline bias in antibody modeling, raising non-germline residue prediction accuracy from 26% to 46% and improving conditional generation tradeoffs over EvoProtGrad.
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CollaFuse: Collaborative Diffusion Models
CollaFuse enables collaborative diffusion model training by splitting computation between resource-limited clients and a central server to reduce local burden and raw data sharing.
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Evaluation of Anatomical Shape Priors in Deep Learning-Based Cardiac Multi-Compartment Segmentation
Standard 3D U-Net remains a strong baseline for multi-compartment cardiac segmentation, with handcrafted shape priors yielding at best marginal or negative effects on MM-WHS CT and WHS++ datasets.