LENS shapes low-frequency eigen noise with a lightweight network to enable efficient, high-quality sampling in distilled diffusion models.
Reward-guided iterative refinement in diffusion models at test-time with applications to protein and dna design
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Reformulates constrained black-box optimization as posterior inference in latent space of flow-based models amortized by outsourced diffusion models, claiming superior performance on synthetic and real tasks.
LPDP adds a local re-solving operator to edit-flow DNA generators so that reward signals can guide insertions, deletions, and substitutions without retraining.
MP2D is a framework that guides discrete diffusion denoising with constrained MCTS and Pareto rewards to optimize protein sequences for four to five simultaneous objectives, outperforming baselines on antimicrobial peptide and binder design tasks.
citing papers explorer
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LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling
LENS shapes low-frequency eigen noise with a lightweight network to enable efficient, high-quality sampling in distilled diffusion models.
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Posterior Inference in Latent Space for Scalable Constrained Black-box Optimization
Reformulates constrained black-box optimization as posterior inference in latent space of flow-based models amortized by outsourced diffusion models, claiming superior performance on synthetic and real tasks.
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LPDP: Inference-Time Reward Control for Variable-Length DNA Generation with Edit Flows
LPDP adds a local re-solving operator to edit-flow DNA generators so that reward signals can guide insertions, deletions, and substitutions without retraining.
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MP2D: Constrained Monte Carlo Tree-Guided Diffusion for Multi-Objective Protein Sequence Design
MP2D is a framework that guides discrete diffusion denoising with constrained MCTS and Pareto rewards to optimize protein sequences for four to five simultaneous objectives, outperforming baselines on antimicrobial peptide and binder design tasks.