16,248 research outputs found
EDEN: Evolutionary Deep Networks for Efficient Machine Learning
Deep neural networks continue to show improved performance with increasing
depth, an encouraging trend that implies an explosion in the possible
permutations of network architectures and hyperparameters for which there is
little intuitive guidance. To address this increasing complexity, we propose
Evolutionary DEep Networks (EDEN), a computationally efficient
neuro-evolutionary algorithm which interfaces to any deep neural network
platform, such as TensorFlow. We show that EDEN evolves simple yet successful
architectures built from embedding, 1D and 2D convolutional, max pooling and
fully connected layers along with their hyperparameters. Evaluation of EDEN
across seven image and sentiment classification datasets shows that it reliably
finds good networks -- and in three cases achieves state-of-the-art results --
even on a single GPU, in just 6-24 hours. Our study provides a first attempt at
applying neuro-evolution to the creation of 1D convolutional networks for
sentiment analysis including the optimisation of the embedding layer.Comment: 7 pages, 3 figures, 3 tables and see video
https://vimeo.com/23451009
Management control in the transfer pricing tax compliant multinational enterprise
This paper studies the impact of transfer pricing tax compliance on management control system (MCS) design and use within one multinational enterprise (MNE) which employed the same transfer prices for tax compliance and internal management purposes. Our analysis shows immediate effects of tax compliance on the design of organising controls with subsequent effects on planning, evaluating and rewarding controls which reveal a more coercive use of the MCS overall. We argue that modifications to the MCS cannot be understood without an appreciation of the MNEs’ fiscal transfer pricing compliance process
Highly robust error correction by convex programming
This paper discusses a stylized communications problem where one wishes to
transmit a real-valued signal x in R^n (a block of n pieces of information) to
a remote receiver. We ask whether it is possible to transmit this information
reliably when a fraction of the transmitted codeword is corrupted by arbitrary
gross errors, and when in addition, all the entries of the codeword are
contaminated by smaller errors (e.g. quantization errors).
We show that if one encodes the information as Ax where A is a suitable m by
n coding matrix (m >= n), there are two decoding schemes that allow the
recovery of the block of n pieces of information x with nearly the same
accuracy as if no gross errors occur upon transmission (or equivalently as if
one has an oracle supplying perfect information about the sites and amplitudes
of the gross errors). Moreover, both decoding strategies are very concrete and
only involve solving simple convex optimization programs, either a linear
program or a second-order cone program. We complement our study with numerical
simulations showing that the encoder/decoder pair performs remarkably well.Comment: 23 pages, 2 figure
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