arXiv:2606. 15637v1 Announce Type: new Abstract: A digital twin (DT) of a patient-specific heart offers significant potential in personalized medicine.
PALATE is a new framework that improves the evaluation of deep generative models by applying the law of total expectation to accessible data. It builds on the Feature Likelihood Divergence (FLD) method but overcomes FLD’s computational limits, integrating with the MMD baseline metric and the DINOv3 feature extractor. Experiments show PALATE matches or surpasses state‑of‑the‑art methods while being more efficient and scalable, especially for detecting memorization and assessing generalization.
arXiv:2608. 10096v1 Announce Type: cross Abstract: Modern data science increasingly gives rise to hypothesis-testing problems that are not naturally formulated in terms of parameters within prespecified statistical models.
arXiv:2607. 00926v1 Announce Type: cross Abstract: Generalizing machine learning models to environments that differ from their training distribution remains a critical hurdle, particularly when data from the target domain is entirely or partially unavailable.
arXiv:2608. 09523v1 Announce Type: new Abstract: Deep neural network (DNN) training with stochastic gradient descent (SGD) and its variants achieves strong empirical performance, yet classical optimization theory does not fully explain this success.
arXiv:2609.24328v1 Announce Type: new Abstract: Combining predictions from different models can improve performance at machine learning tasks, but the training of the individual models and the rule u...
arXiv:2608.09523v2 Announce Type: replace Abstract: Stochastic gradient descent (SGD) and its variants train deep neural networks (DNNs) effectively in practice, yet classical optimization theory doe...
arXiv:2607. 19332v1 Announce Type: new Abstract: Generative models have undergone many generations of evolution, from VAEs/GANs to diffusion/flow matching.
arXiv:2606. 11247v1 Announce Type: cross Abstract: Generative models are increasingly used to propose designs, data, and control actions for physical systems, yet many such systems are governed by hard physical constraints rather than by perceptual plausibility.
arXiv:2607. 04360v1 Announce Type: cross Abstract: Conditional generative models have emerged as powerful tools for sampling from target conditional distributions, driving substantial advances across a wide range of scientific and applied domains.
The paper introduces a flow matching framework that unifies various neural representational dissimilarity metrics under a single theoretical umbrella. By interpreting these metrics as Jeffreys divergences with different velocity constraints, the authors demonstrate that flow matching improves distance estimation for complex distributions and continuous variables. The framework also facilitates the principled design of new dissimilarity measures.
arXiv:2606. 21385v2 Announce Type: replace-cross Abstract: This paper explores unsupervised disentangled representation learning from a functional perspective.