Gromov--Wasserstein
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Related Articles from SNS
Network Learning with Semi-relaxed Gromov-Wasserstein
arXiv:2606.02223v1 Announce Type: new Abstract: Estimating the generative mechanism of large-scale networks is a fundamental challenge in statistical machine learning. It requires the identification of the latent connectivity structure, which is in general an NP-hard combinatorial problem due to the absence of canonical node labels. We address this challenge by allowing for probabilistic couplings, thereby relaxing the assignment problem.
Morphology-robust quantification of subcellular organization in complex cells
Quantitative analysis of subcellular protein organization is often confounded by variation in cell morphology, limiting the identification and interpretation of localization patterns in fluorescence microscopy data from morphologically complex cells, such as neurons and glia. We introduce CellAligner, an unsupervised framework that uses fused unbalanced Gromov-Wasserstein couplings to map protein distributions from morphologically distinct cells into shared anchor-cell geometries, enabling...
TERRA: Task-Embedded Reasoning and Representation Architecture for Cross-Domain Applications
Announce Type: new Abstract: A single action-conditioned latent predictive architecture can in principle be trained on the structured state of a driving scene, a robot workspace, or a financial order book. The ingredients for doing so within any one domain already exist and are individually validated: masked-latent prediction, action-conditioned latent world models, discrete action tokenization, and joint-embedding prediction on voxelized state. What is not established, and what TERRA...
Convex Distance Operator Transport: A Convex and Geometry-Preserving Formulation
arXiv:2606.02047v1 Announce Type: cross Abstract: We introduce Convex Distance Operator Transport (CDOT), the first convex optimal transport framework that aligns distributions across heterogeneous domains by jointly preserving feature correspondence and intrinsic geometric structure. Specifically, CDOT employs an operator-based regularization that aligns aggregated distance structures by introducing distance and conditional expectation operators. Consequently, the proposed regularization...
A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation
Announce Type: new Abstract: Graph foundation models aim to learn transferable knowledge from diverse graphs for generalization to unseen graphs and tasks. Unlike text and images, graphs lack a shared vocabulary or regular spatial grid, making cross-graph transfer challenging. This challenge comes from both feature discrepancies and, more critically, diverse graph structures.