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A-vit: Adaptive tokens for efficient vision transformer
We introduce A-ViT, a method that adaptively adjusts the inference cost of vision transformer
ViT for images of different complexity. A-ViT achieves this by automatically reducing the …
ViT for images of different complexity. A-ViT achieves this by automatically reducing the …
Dyrep: Bootstrap** training with dynamic re-parameterization
Structural re-parameterization (Rep) methods achieve noticeable improvements on simple
VGG-style networks. Despite the prevalence, current Rep methods simply re-parameterize …
VGG-style networks. Despite the prevalence, current Rep methods simply re-parameterize …
Implicit regularization of deep residual networks towards neural ODEs
Residual neural networks are state-of-the-art deep learning models. Their continuous-depth
analog, neural ordinary differential equations (ODEs), are also widely used. Despite their …
analog, neural ordinary differential equations (ODEs), are also widely used. Despite their …
[HTML][HTML] A hybrid quantum–classical neural network with deep residual learning
Inspired by the success of classical neural networks, there has been tremendous effort to
develop classical effective neural networks into quantum concept. In this paper, a novel …
develop classical effective neural networks into quantum concept. In this paper, a novel …
Adavit: Adaptive tokens for efficient vision transformer
We introduce A-ViT, a method that adaptively adjusts the inference cost of vision transformer
(ViT) for images of different complexity. A-ViT achieves this by automatically reducing the …
(ViT) for images of different complexity. A-ViT achieves this by automatically reducing the …