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Learning Invariant Causal Mechanism from Vision-Language Models
To address this, we propose the Invariant Causal Mechanism of CLIP (CLIP-ICM), an algorithm designed to provably identify invariant latent factors with the aid of interventional data, and perform accurate prediction on various domains.
Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning
In this study, we utilized statistical analysis and machine learning methods to examine whether rehabilitation exercises can improve patients post-stroke functional abilities, as well as forecast the improvement in functional abilities.
Landmark-Guided Cross-Speaker Lip Reading with Mutual Information Regularization
Lip reading, the process of interpreting silent speech from visual lip movements, has gained rising attention for its wide range of realistic applications.
Probing Script Knowledge from Pre-Trained Models
Script knowledge is critical for humans to understand the broad daily tasks and routine activities in the world.
Finite response time in stripe formation by bacteria with density-suppressed motility
We propose a model that includes a simplified regulatory dynamics of the tumbling frequency in individual cells to clarify the role of finite response time.
