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.

Existing image-text modality alignment in Vision Language Models (VLMs) treats each text token equally in an autoregressive manner.

Model editing has become an increasingly popular method for efficiently updating knowledge within language models. Current approaches primarily focus on reliability, generalization, and locality, with many excelling across these criteria.

Lip reading aims at transforming the videos of continuous lip movement into textual contents, and has achieved significant progress over the past decade. It serves as a critical yet practical assistance for speech-impaired individuals, with more practicability than speech recognition in noisy environments.

Recent advances in microscopy have enabled the rapid generation of terabytes of image data in cell biology and biomedical research.

Bi-narization, an ultra-compression algorithm, offers the potential for effectively accelerating DMs.