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pro vyhledávání: '"Ai, Qingzhong"'
Learning with imbalanced data is a challenging problem in deep learning. Over-sampling is a widely used technique to re-balance the sampling distribution of training data. However, most existing over-sampling methods only use intra-class information
Externí odkaz:
http://arxiv.org/abs/2302.10910
Recently, tile pruning has been widely studied to accelerate the inference of deep neural networks (DNNs). However, we found that the loss due to tile pruning, which can eliminate important elements together with unimportant elements, is large on tra
Externí odkaz:
http://arxiv.org/abs/2207.14545
Stein variational gradient descent (SVGD) and its variants have shown promising successes in approximate inference for complex distributions. In practice, we notice that the kernel used in SVGD-based methods has a decisive effect on the empirical per
Externí odkaz:
http://arxiv.org/abs/2107.09338
Recent studies show that advanced priors play a major role in deep generative models. Exemplar VAE, as a variant of VAE with an exemplar-based prior, has achieved impressive results. However, due to the nature of model design, an exemplar-based model
Externí odkaz:
http://arxiv.org/abs/2107.09286
Publikováno v:
In Neural Networks October 2023 167:706-714
Publikováno v:
In Neurocomputing 21 January 2023 518:122-132
Akademický článek
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Publikováno v:
Cognitive Computation; Mar2023, Vol. 15 Issue 2, p672-682, 11p