The complete works of William Shakespeare : comprising his plays and poems, also the history of his life, his will and an introduction to each play to which is added an index to the characters

By W. Shakespeare

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In Social Sciences & Humanities

Citations in the corpus

Fair Resource Allocation in Federated Learning (2019, methodology): “Finally, we explore text data built from The Complete Works of William Shakespeare [24, 42] where each speaking role is associated with a device.”
Federated Learning with Non-IID Data (2018, background): “MNIST [6] and CIFAR-10 [7]), and LSTM on a language dataset [8].”
Federated Meta-Learning with Fast Convergence and Efficient Communication (2018, background): “(2) Shakespeare for next character prediction, which is built from The Complete Works of William Shakespeare [21].”
Adversarial Ranking for Language Generation (2017, methodology): “In this experiment, we train our model on the Romeo and Juliet play [28] to further validate the proposed method.”
Federated Learning Based on Dynamic Regularization (2021, methodology): “, 2017) (EMNIST-L), Shakespeare (Shakespeare, 1994) as well as a synthetic dataset.”
Enacting the ‘true self’: Towards a theory of embodied authentic leadership (2010, background): “(Shakespeare, 1936 p. 1150) As Clurman describes it, “When an actor exhibits sorrow or rage, nobility or exaltation, without our feeling something of their reality within him, wewho employ the vocabulary of the system say he is playing the result: he is demonstrating what hewants to believe he is…”
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication (2018, methodology): “The Shakespeare dataset consists of the complete works of William Shakespeare [28] concatenated to a sequence of 5072443 training and 105675 test characters.”
CMFL: Mitigating Communication Overhead for Federated Learning (2019, methodology): “Specifically, we develop the training dataset from The Complete Works of William Shakespeare [29].”