2,447 research outputs found
Unfolding and Shrinking Neural Machine Translation Ensembles
Ensembling is a well-known technique in neural machine translation (NMT) to
improve system performance. Instead of a single neural net, multiple neural
nets with the same topology are trained separately, and the decoder generates
predictions by averaging over the individual models. Ensembling often improves
the quality of the generated translations drastically. However, it is not
suitable for production systems because it is cumbersome and slow. This work
aims to reduce the runtime to be on par with a single system without
compromising the translation quality. First, we show that the ensemble can be
unfolded into a single large neural network which imitates the output of the
ensemble system. We show that unfolding can already improve the runtime in
practice since more work can be done on the GPU. We proceed by describing a set
of techniques to shrink the unfolded network by reducing the dimensionality of
layers. On Japanese-English we report that the resulting network has the size
and decoding speed of a single NMT network but performs on the level of a
3-ensemble system.Comment: Accepted at EMNLP 201
Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles
The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has
received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking
received support from the European Union’s Horizon 2020 research and innovation programme and Germany,
Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy,
Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL
Joint Undertaking under grant agreement No. 692455-2
Spinal cord gray matter segmentation using deep dilated convolutions
Gray matter (GM) tissue changes have been associated with a wide range of
neurological disorders and was also recently found relevant as a biomarker for
disability in amyotrophic lateral sclerosis. The ability to automatically
segment the GM is, therefore, an important task for modern studies of the
spinal cord. In this work, we devise a modern, simple and end-to-end fully
automated human spinal cord gray matter segmentation method using Deep
Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate
our method against six independently developed methods on a GM segmentation
challenge and report state-of-the-art results in 8 out of 10 different
evaluation metrics as well as major network parameter reduction when compared
to the traditional medical imaging architectures such as U-Nets.Comment: 13 pages, 8 figure
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