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Found 14 papers, 7 papers with code
Feedback Efficient Online Fine-Tuning of Diffusion Models
It is natural to frame this as a reinforcement learning (RL) problem, in which the objective is to fine-tune a diffusion model to maximize a reward function that corresponds to some property.
Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control
Diffusion models excel at capturing complex data distributions, such as those of natural images and proteins.
Unsupervised Segmentation of Colonoscopy Images
Colonoscopy plays a crucial role in the diagnosis and prognosis of various gastrointestinal diseases.
Conformalized Deep Splines for Optimal and Efficient Prediction Sets
SPICE is compatible with two different efficient-to-compute conformal scores, one oracle-optimal for marginal coverage (SPICE-ND) and the other asymptotically optimal for conditional coverage (SPICE-HPD).
Complex Preferences for Different Convergent Priors in Discrete Graph Diffusion
Diffusion models have achieved state-of-the-art performance in generating many different kinds of data, including images, text, and videos.
RINGER: Rapid Conformer Generation for Macrocycles with Sequence-Conditioned Internal Coordinate Diffusion
Macrocyclic peptides are an emerging therapeutic modality, yet computational approaches for accurately sampling their diverse 3D ensembles remain challenging due to their conformational diversity and geometric constraints.
CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning
Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization.
Towards Understanding and Improving GFlowNet Training
We investigate how to learn better flows, and propose (i) prioritized replay training of high-reward $x$, (ii) relative edge flow policy parametrization, and (iii) a novel guided trajectory balance objective, and show how it can solve a substructure credit assignment problem.
GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusion
Our framework for graph diffusion can have a large impact on the interpretable conditional generation of graphs, including the generation of drug-like molecules with desired properties in a way which is informed by experimental evidence.
Improving Graph Generation by Restricting Graph Bandwidth
However, one of the main limitations of existing methods is their large output space, which limits generation scalability and hinders accurate modeling of the underlying distribution.
NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning
Learning causal relationships between variables is a well-studied problem in statistics, with many important applications in science.
Hierarchically branched diffusion models leverage dataset structure for class-conditional generation
We highlight several advantages of branched diffusion models over the current state-of-the-art methods for class-conditional diffusion, including extension to novel classes in a continual-learning setting, a more sophisticated form of analogy-based conditional generation (i. e. transmutation), and a novel interpretability into the generation process.
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning
Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property prediction.
Conditional Diffusion with Less Explicit Guidance via Model Predictive Control
How much explicit guidance is necessary for conditional diffusion?
