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On the Importance of Contrastive Loss in Multimodal Learning
Recently, contrastive learning approaches (e. g., CLIP (Radford et al., 2021)) have received huge success in multimodal learning, where the model tries to minimize the distance between the representations of different views (e. g., image and its caption) of the same data point while keeping the representations of different data points away from each other.
Depth Separation with Multilayer Mean-Field Networks
Depth separation -- why a deeper network is more powerful than a shallower one -- has been a major problem in deep learning theory.
Understanding Deflation Process in Over-parametrized Tensor Decomposition
In this paper we study the training dynamics for gradient flow on over-parametrized tensor decomposition problems.
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