However, the integration of heterogeneous multimodal data poses a significant challenge, as confounding effects and dependencies among such heterogeneous data sources introduce unwanted variability and bias, leading to suboptimal performance of multimodal models. Therefore, it becomes crucial to normalize the low-or high-level features extracted from data modalities before their fusion takes place.

Previous deep learning approaches for survival analysis have primarily relied on ranking losses to improve discrimination performance, which often comes at the expense of calibration performance. To address such an issue, we propose a novel contrastive learning approach specifically designed to enhance discrimination without sacrificing calibration.

Recently there has been significant theoretical progress on understanding the convergence andgeneralization ofgradient-based methods onnonconvexlosses withoverparameterized models. Nevertheless, manyaspectsofoptimization and generalization and in particular the critical role of small random initialization are not fully understood.

Recently there has been significant theoretical progress on understanding the convergence andgeneralization ofgradient-based methods onnonconvexlosses withoverparameterized models. Nevertheless, manyaspectsofoptimization and generalization and in particular the critical role of small random initialization are not fully understood.