TILDE derives a minimum-deviation, energy-tilted target distribution for concept unlearning in diffusion models and realizes it via residual ∇-GFlowNet training.
Score-based generative modeling through stochastic differential equations
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.
DiffSketcher synthesizes vector sketches from natural language by optimizing Bezier curves with diffusion model guidance via extended SDS loss.
i-DEQ adds momentum to DEQ fixed-point iterations, yielding convergence guarantees, training stability, and halved inference time while matching state-of-the-art reconstruction quality on inverse problems.
Flow Matching is a generative modeling framework with mathematical foundations, design choices, extensions, and open-source PyTorch code for applications like image and text generation.
citing papers explorer
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TILDE: TILt-based Distributional Erasure for Concept Unlearning
TILDE derives a minimum-deviation, energy-tilted target distribution for concept unlearning in diffusion models and realizes it via residual ∇-GFlowNet training.
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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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DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models
DiffSketcher synthesizes vector sketches from natural language by optimizing Bezier curves with diffusion model guidance via extended SDS loss.
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i-DEQ: A stable inertial deep equilibrium model for image restoration
i-DEQ adds momentum to DEQ fixed-point iterations, yielding convergence guarantees, training stability, and halved inference time while matching state-of-the-art reconstruction quality on inverse problems.
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Flow Matching Guide and Code
Flow Matching is a generative modeling framework with mathematical foundations, design choices, extensions, and open-source PyTorch code for applications like image and text generation.