arXiv:2608. 11054v1 Announce Type: new Abstract: Deep learning models have emerged as the standard computational tool for a wide range of applications in genomics.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2607. 28248v1 Announce Type: cross Abstract: The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures lack principled uncertainty quantification.
arXiv:2607. 06776v1 Announce Type: new Abstract: We introduce an efficient Bayesian deep ensemble method for predictive regression designed to enhance interpretability while maintaining competitive predictive performance and computational efficiency.
We introduce an efficient Bayesian deep ensemble method for predictive regression designed to enhance interpretability while maintaining competitive predictive performance and computational efficiency. Our method combines the statistical rigor of Bayesian inference with the scalability of deep ensembles, providing calibrated uncertainty estimates that enable its use not only for standalone prediction but also as a component within broader learning systems.
arXiv:2606. 13818v1 Announce Type: new Abstract: This thesis investigates how Bayesian principles can deepen our understanding of modern deep learning systems.
The paper introduces DUO, a framework for Deep Imbalanced Regression that models each prediction as a conditional Gaussian to capture instance‑level uncertainty. By decoupling mean and variance optimization, DUO enhances learning signals for tail samples and mitigates gradient coupling that hampers hard examples. A distribution‑guided contrastive learning component further refines feature representations, leading to state‑of‑the‑art performance on several visual and biological regression benchmarks.
arXiv:2606. 01468v1 Announce Type: cross Abstract: Due to their explicit priors and ability to model uncertainty, Bayesian methods have played a major role in dynamical latent variable modeling of single-cell neural recordings.