arXiv:2604. 27967v2 Announce Type: replace Abstract: Background: We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty.
By Ivan Lerner, Jean Feydy, Alexandre Kalimouttou, Anita Burgun, Francis Bach
The study presents a curated dataset of 616 fresh embryo-transfer cycles and develops a Hybrid Attention Network (HAN) combined with logistic regression to predict live birth after IVF/ICSI. The HAN+LR ensemble achieved the best performance with an AUC‑ROC of 0.710, outperforming both individual components and other machine‑learning baselines. The differentially weighted trophectoderm morphology emerged as the most important predictor across models.
By Abdul Raheem Shahzad, Jinxin Chen, Ming Zhang, Juan Deng
Simulation-based inference is challenging when many heterogeneous observations must be composed, hierarchical latent structure must be preserved, and the simulator is misspecified relative to observed...
arXiv:2609.36950v1 Announce Type: new
Abstract: Simulation-based inference is challenging when many heterogeneous observations must be composed, hierarchical latent structure must be preserved, and t...
By Vincent D. Zaballa, Elliot E. Hui
arXiv:2608. 16507v1 Announce Type: new Abstract: Due to the limited amount of information, modeling longitudinal rare-disease data can benefit from integrating clinical knowledge.
By Clemens Sch\"achter, Astrid Pechmann, Janbernd Kirschner, Jan Hasenauer, Harald Binder
arXiv:2608. 00073v1 Announce Type: cross Abstract: Rigorous dataset partitioning is a foundational, yet frequently overlooked, prerequisite for reliable deep learning in longitudinal medical imaging.
By Qinghui Liu, Jon Andr\'e Ottesen, Atle Bj{\o}rnerud, Kyrre Eeg Emblem