Results 41 to 50 of about 7,675,115 (371)
IEEE Transactions on Emerging Topics in Computational Intelligence, 2019 Equality and incomparability multi-label ranking have not been introduced to learning before. This paper proposes new native ranker neural network to address the problem of multi-label ranking including incomparable preference orders using a new activation and error functions and new architecture.
Ayman Elgharabawy +2 more
openaire +7 more sources
Frontiers in Communication, 2017 Accumulating evidence suggests that category representations are based on features. Distinguishing features are considered to define categories, because of all-or-none responses for objects in different categories; however, it is unclear how ...
Takahiro Soshi +3 more
doaj +1 more source
Optimal rates of approximation by shallow ReLU$^k$ neural networks and applications to nonparametric regression [PDF]
, 2023 We study the approximation capacity of some variation spaces corresponding to shallow ReLU$^k$ neural networks. It is shown that sufficiently smooth functions are contained in these spaces with finite variation norms. For functions with less smoothness, the approximation rates in terms of the variation norm are established.
arxiv +1 more source
Quantitative models reveal the organization of diverse cognitive functions in the brain
Nature Communications, 2020 The authors construct quantitative models of human brain activity evoked by 103 cognitive tasks and reveal the organization of diverse cognitive functions in the brain.
Tomoya Nakai, Shinji Nishimoto
doaj +1 more source
Understanding Vector-Valued Neural Networks and Their Relationship with Real and Hypercomplex-Valued Neural Networks [PDF]
, 2023 Despite the many successful applications of deep learning models for multidimensional signal and image processing, most traditional neural networks process data represented by (multidimensional) arrays of real numbers. The intercorrelation between feature channels is usually expected to be learned from the training data, requiring numerous parameters ...
arxiv +1 more source
Neural Networks proposes to reconstruct situated practices, social histories, mediating techniques, and ontological assumptions that inform the computational project of the same name. If so-called machine learning comprises a statistical approach to pattern extraction, then neural networks can be defined as a biologically inspired model that relies on ...
Dhaliwal, Ranjodh Singh +2 more
openaire +2 more sources
Applied Sciences, 2019 High-maneuvering target tracking is a focused application area in radar positioning and military defense systems, especially in three-dimensional space.
Qi Deng, Gang Chen, Huaxiang Lu
doaj +1 more source
Frontiers in Neuroergonomics, 2021 The mechanical parameters of keyboard switches affect the psychological sense of pressing. The effects of different mechanical parameters on psychological sense have been quantified using questionnaires, but these subjective evaluations are unable to ...
Hiroki Watanabe +10 more
doaj +1 more source
Guaranteed Quantization Error Computation for Neural Network Model Compression [PDF]
arXiv, 2023 Neural network model compression techniques can address the computation issue of deep neural networks on embedded devices in industrial systems. The guaranteed output error computation problem for neural network compression with quantization is addressed in this paper. A merged neural network is built from a feedforward neural network and its quantized
arxiv
Gender differences in guilt aversion in Korea and the United Kingdom
Scientific Reports, 2022 Guilt aversion, which describes the tendency to reduce the discrepancy between a partner’s expectation and his/her actual outcome, is a key driving force for cooperation in both the East and West.
Tsuyoshi Nihonsugi +2 more
doaj +1 more source