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Found 9 papers, 4 papers with code
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation
We hence establish UOT-FM as a principled method for unpaired image translation.
Entropic (Gromov) Wasserstein Flow Matching with GENOT
Optimal transport (OT) theory has reshaped the field of generative modeling: Combined with neural networks, recent \textit{Neural OT} (N-OT) solvers use OT as an inductive bias, to focus on ``thrifty'' mappings that minimize average displacement costs.
MAGNet: Motif-Agnostic Generation of Molecules from Shapes
Recent advances in machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions.
The power of motifs as inductive bias for learning molecular distributions
Machine learning for molecules holds great potential for efficiently exploring the vast chemical space and thus streamlining the drug discovery process by facilitating the design of new therapeutic molecules.
Uncertainty Quantification for Atlas-Level Cell Type Transfer
Here, for the first time, we introduce uncertainty quantification methods for cell type classification on single-cell reference atlases.
Sparsity in Continuous-Depth Neural Networks
Neural Ordinary Differential Equations (NODEs) have proven successful in learning dynamical systems in terms of accurately recovering the observed trajectories.
FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation
One of the most common methods for analyzing CT and MRI images for diagnosis and follow-up treatment is liver segmentation.
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells.
Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields
A statistical analysis of these measurements requires annotation of mitosis events, which is currently a tedious and time-consuming task that has to be performed manually.
