Search Results for author:
Found 11 papers, 5 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.
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.
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.
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?
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction
Advances in deep neural network (DNN) based molecular property prediction have recently led to the development of models of remarkable accuracy and generalization ability, with graph convolution neural networks (GCNNs) reporting state-of-the-art performance for this task.
