Results 151 to 160 of about 67,964 (199)
QSyncFold: quantum neural network for multidimensional sync-discovery in protein folding. [PDF]
Brief BioinformShi J +5 more
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Enhanced ResU-Net for brain tumor segmentation using EfficientNetB0, channel attention, and ASPP. [PDF]
Biomed Phys Eng ExpressBehzadpour M, Azizi E, Ortiz BL, Wu K.
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Generative Principal Component Regression via Variational Inference. [PDF]
IEEE Trans Signal ProcessTalbot A +7 more
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International Journal of Foundations of Computer Science, 2009
The purpose of this paper is to present various algebraic views of multisets, and certain connections between the theory of multisets (with multiplicities in the semiring of positive integers) and natural computing, in particular membrane and DNA computing.
Gabriel Ciobanu, Viorel Mihai Gontineac
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The purpose of this paper is to present various algebraic views of multisets, and certain connections between the theory of multisets (with multiplicities in the semiring of positive integers) and natural computing, in particular membrane and DNA computing.
Gabriel Ciobanu, Viorel Mihai Gontineac
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Multiterminal source encoding with encoder breakdown
IEEE Transactions on Information Theory, 1989Summary: When separate encoders are assigned to each of two correlated sources, it is in general true that the rate sum required is less when decoding is done by a single decoder than when separate decoders are used. However, if either encoder breaks down, system performance ordinarily degreades severely in the single-decoder case when one tries to ...
Toby Berger, Raymond W. Yeung
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2021 International Joint Conference on Neural Networks (IJCNN), 2021
A salient problem in machine learning is transforming categorical variables into efficient numerical features. This focus is warranted due to the ubiquity of categorical data in real-world applications but, on the contrary, the development of many machine learning methods based on the assumption of having numerical variables.
Kassymzhomart Kunanbayev +2 more
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A salient problem in machine learning is transforming categorical variables into efficient numerical features. This focus is warranted due to the ubiquity of categorical data in real-world applications but, on the contrary, the development of many machine learning methods based on the assumption of having numerical variables.
Kassymzhomart Kunanbayev +2 more
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Proceedings of the 5th International Conference on Digital Libraries for Musicology, 2018
In this paper, we discuss how different encodings in symbolic music files can have consequences for music analysis, where a truthful representation, not only of the musical score, but of the semantics of the music, can change the results of music analysis tools.
Néstor Nápoles +2 more
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In this paper, we discuss how different encodings in symbolic music files can have consequences for music analysis, where a truthful representation, not only of the musical score, but of the semantics of the music, can change the results of music analysis tools.
Néstor Nápoles +2 more
openaire +1 more source