Results 51 to 60 of about 204,288 (176)
A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models
, 2017 Beam search is a desirable choice of test-time decoding algorithm for neural sequence models because it potentially avoids search errors made by simpler greedy methods.
Berg-Kirkpatrick, Taylor +3 more
core +1 more source
IEEE Access, 2019 This paper addressed the vessel segmentation and disease diagnostic in coronary angiography image and proposed an Encoder-Decoder architecture of deep learning with End-to-End model, where Encoder is based on ResNet, and the deep features are exacted ...
Shiwen Pan +7 more
doaj +1 more source
Combinatorial Information Theory: I. Philosophical Basis of Cross-Entropy and Entropy [PDF]
, 2007 This study critically analyses the information-theoretic, axiomatic and combinatorial philosophical bases of the entropy and cross-entropy concepts. The combinatorial basis is shown to be the most fundamental (most primitive) of these three bases, since ...
Niven, Robert K.
core
Entropy, 2016 Virtualization technologies make it possible for cloud providers to consolidate multiple IaaS provisions into a single server in the form of virtual machines (VMs). Additionally, in order to fulfill the divergent service requirements from multiple users,
Li Pan, Datao Wang
doaj +1 more source
Optimal staffing under an annualized hours regime using Cross-Entropy optimization [PDF]
, 2012 This paper discusses staffing under annualized hours. Staffing is the selection of the most cost-efficient workforce to cover workforce demand. Annualized hours measure working time per year instead of per week, relaxing the restriction for employees to ...
Boucherie, Richard J. +2 more
core +2 more sources
, 2016 Information-theoretic measures such as the entropy, cross-entropy and the Kullback-Leibler divergence between two mixture models is a core primitive in many signal processing tasks.
Nielsen, Frank, Sun, Ke
core +2 more sources
Approximating the Gradient of Cross-Entropy Loss Function
IEEE Access, 2020 A loss function has two crucial roles in training a conventional discriminant deep neural network (DNN): (i) it measures the goodness of classification and (ii) generates the gradients that drive the training of the network. In this paper, we approximate
Li Li +2 more
doaj +1 more source
Recommender systems fairness evaluation via generalized cross entropy [PDF]
, 2019 Fairness in recommender systems has been considered with respect to sensitive attributes of users (e.g., gender, race) or items (e.g., revenue in a multistakeholder setting).
Anelli, Vito Walter +4 more
core +1 more source
Taming the Cross Entropy Loss [PDF]
, 2019 We present the Tamed Cross Entropy (TCE) loss function, a robust derivative of the standard Cross Entropy (CE) loss used in deep learning for classification tasks. However, unlike other robust losses, the TCE loss is designed to exhibit the same training properties than the CE loss in noiseless scenarios.
Martinez, Manuel, Stiefelhagen, Rainer
openaire +3 more sources
A Novel Protection Scheme for MMC-HVdc Transmission Lines Based on Cross-Entropy of Charge
IEEE Access, 2020 MMC-HVdc transmission system has become the mainstream of power construction and plays an increasingly important role in the power system. However, the dc line fault results in shutting down of the converters, which can greatly endanger the security of ...
Chuanjian Wu, Dahai Zhang, Jinghan He
doaj +1 more source