Abstract: This paper proposes a precise signal recovery method utilizing a composition of multiple non-convex regularization functions, termed multilayered non-convex regularization functions, to ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. We present a comprehensive investigation of the El-QDO embedding method [Phys. Rev.

The paper studies a regularization of the quantum (effective) action for a scalar field theory in a general position on a compact smooth Riemannian manifold. As the main method, we propose the use of ...

I am currently working with your TrajectoryNet implementation and have a question regarding the loss function configuration. In the paper, the overall loss comprises the negative log-likelihood term, ...

Abstract: This article investigates the use of extended Kalman filtering to train recurrent neural networks with rather general convex loss functions and regularization terms on the network parameters ...

When attempting the question, there is a bonus part to add l2 regularization to the softmax regression code (In [75]): According to the book, in the section about Ridge Regression, we are supposed to ...

Deep learning has been highly successful in recent years and has led to dramatic improvements in multiple domains. Deep-learning algorithms often generalize quite well in practice, namely, given ...

Individual wants (preferences) and abilities may partly determine prices or tax rates. The resulting simultaneous changes in prices (or taxes) and preferences make it difficult to estimate policy ...

1 Department of Mechanical Engineering, University of Maryland, College Park, College Park, MD, United States 2 Office of Science and Engineering Laboratories, Center for Devices and Radiological ...