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Found 6 papers, 2 papers with code
Contrastive Learning on Multimodal Analysis of Electronic Health Records
To accommodate the statistical analysis of multimodal EHR data, in this paper, we propose a novel multimodal feature embedding generative model and design a multimodal contrastive loss to obtain the multimodal EHR feature representation.
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
The surge in multimodal AI's success has sparked concerns over data privacy in vision-and-language tasks.
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data
We show that the algorithm can detect the ground-truth pairs and improve performance by fully exploiting unpaired datasets.
The Power of Contrast for Feature Learning: A Theoretical Analysis
Contrastive learning has achieved state-of-the-art performance in various self-supervised learning tasks and even outperforms its supervised counterpart.
Asymptotic Risk of Overparameterized Likelihood Models: Double Descent Theory for Deep Neural Networks
We investigate the asymptotic risk of a general class of overparameterized likelihood models, including deep models.
Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality
In this study, we prove that an intrinsic low dimensionality of covariates is the main factor that determines the performance of deep neural networks (DNNs).
