For each prediction task, we select features that have a causal influence on the target of prediction.

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.

For what purpose was the dataset created? Was there a specific task in mind? YFCC with 100 million image/videos and associated metadata. Who created the dataset (e.g., which team, research group) and on behalf of which Who funded the creation of the dataset? This work was sponsored by Hugging Face and Stability AI. What do the instances that comprise the dataset represent (e.g., documents, photos, Are there multiple types of instances (e.g., movies, users, and ratings; We provide 5.8 billion image-text pairs.

'Scale the model, scale the data, scale the compute' is the reigning sentiment in the world of generative AI today.

Generative AI and large language models hold great promise in enhancing programming education by generating individualized feedback and hints for learners. Recent works have primarily focused on improving the quality of generated feedback to achieve human tutors' quality.