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Found 9 papers, 5 papers with code
A Simple and Scalable Representation for Graph Generation
Recently, there has been a surge of interest in employing neural networks for graph generation, a fundamental statistical learning problem with critical applications like molecule design and community analysis.
Drug Discovery with Dynamic Goal-aware Fragments
Additionally, the existing fragment-based generative models cannot update the fragment vocabulary with goal-aware fragments newly discovered during the generation.
Toward Automated Detection of Microbleeds with Anatomical Scale Localization: A Complete Clinical Diagnosis Support Using Deep Learning
The anatomical localization task does not only tell to which region the CMBs belong but also eliminate some FPs from the detection task by utilizing anatomical information.
Exploring Chemical Space with Score-based Out-of-distribution Generation
A well-known limitation of existing molecular generative models is that the generated molecules highly resemble those in the training set.
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
Specifically, we propose a new graph diffusion process that models the joint distribution of the nodes and edges through a system of stochastic differential equations (SDEs).
Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRI
We present an intensive analysis using various types of image priors: the proposed self-assisted priors and priors from other image contrast of the same subject.
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation
We further show with ablation studies that our method, predictive error-PER (FREED(PE)), significantly improves the model performance.
MOG: Molecular Out-of-distribution Generation with Energy-based Models
To validate the ability to explore the chemical space beyond the known molecular distribution, we experiment with MOG to generate molecules of high absolute values of docking score, which is the affinity score based on a physical binding simulation between a target protein and a given molecule.
Edge Representation Learning with Hypergraphs
This dual hypergraph construction allows us to apply message-passing techniques for node representations to edges.
