Improved brain pattern recovery through ranking approaches

Implementing the TM pattern in this automated way significantly helps verify the TrAM framework in that it brought about refine- ment in the type hierarchy aligning categories in

Abstract—In this paper, we propose a successive convex approximation framework for sparse optimization where the nonsmooth regularization function in the objective function is

The extraction of lexico-grammatical patterns from the attention mechanisms allowed our model trained on the bank reports to produce more accurate and domain-specific summaries..

Our goal is, on one hand, to write down explicit first and second order optimality conditions for general 2-dimensional shape optimization problems with convexity con- straint and,

• The third challenge is that the data hiding algorithms should be robust enough to hide the secret information into genuine documents and to detect the hidden information from

The author points out how other characteristics have defied this affirmation in narrative plots in order to reaffirm the personal identity of its main

1) The n-spectrum: The spectrum is truncated at a given degree N defined in (2). The power remaining for degrees n ≥ N in the spectrum must be negligible to ensure a

In this paper, we study the support recovery guarantees of underdetermined sparse regression using the ` 1 -norm as a regularizer and a non-smooth loss function for data