Institution
Technical University of Berlin
Education•Berlin, Germany•
About: Technical University of Berlin is a education organization based out in Berlin, Germany. It is known for research contribution in the topics: Laser & Catalysis. The organization has 27292 authors who have published 59342 publications receiving 1414623 citations. The organization is also known as: Technische Universität Berlin & TU Berlin.
Topics: Laser, Catalysis, Quantum dot, Computer science, Context (language use)
Papers published on a yearly basis
Papers
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Max Delbrück Center for Molecular Medicine1, National University of Singapore2, The Heart Research Institute3, Humboldt University of Berlin4, National Institutes of Health5, Hammersmith Hospital6, Charité7, University of Hamburg8, Utrecht University9, University College London10, Ruhr University Bochum11, University of Sydney12, Technical University of Berlin13, Max Planck Society14, University of Münster15, Harvard University16, Brigham and Women's Hospital17, Howard Hughes Medical Institute18
TL;DR: This work analyzes the translatomes of 80 human hearts to identify new translation events and quantify the effect of translational regulation, and shows extensive translational control of cardiac gene expression, which is orchestrated in a process-specific manner.
361 citations
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01 Jan 2019TL;DR: In this paper, an empirical measure of the approximate accuracy of feature importance estimates in deep neural networks is proposed, and it is shown that ensemble based approaches outperform a random assignment of importance.
Abstract: We propose an empirical measure of the approximate accuracy of feature importance estimates in deep neural networks. Our results across several large-scale image classification datasets show that many popular interpretability methods produce estimates of feature importance that are not better than a random designation of feature importance. Only certain ensemble based approaches---VarGrad and SmoothGrad-Squared---outperform such a random assignment of importance. The manner of ensembling remains critical, we show that some approaches do no better then the underlying method but carry a far higher computational burden.
361 citations
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TL;DR: In this article, the authors investigate the effect of digitalization on energy consumption using an analytical model, and investigate four effects: (1) direct effects from the production, usage and disposal of information and communication technologies (ICT), (2) energy efficiency increases from digitalization, (3) economic growth from increases in labor and energy productivities and (4) sectoral change/tertiarization from the rise of ICT services.
360 citations
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03 Jul 2018TL;DR: In this paper, a single-layer recurrent neural network (WaveRNN) with a dual softmax layer was proposed for text-to-speech synthesis, which achieved state-of-the-art results in audio, visual and textual domains.
Abstract: Sequential models achieve state-of-the-art results in audio, visual and textual domains with respect to both estimating the data distribution and generating high-quality samples. Efficient sampling for this class of models has however remained an elusive problem. With a focus on text-to-speech synthesis, we describe a set of general techniques for reducing sampling time while maintaining high output quality. We first describe a single-layer recurrent neural network, the WaveRNN, with a dual softmax layer that matches the quality of the state-of-the-art WaveNet model. The compact form of the network makes it possible to generate 24kHz 16-bit audio 4x faster than real time on a GPU. Second, we apply a weight pruning technique to reduce the number of weights in the WaveRNN. We find that, for a constant number of parameters, large sparse networks perform better than small dense networks and this relationship holds for sparsity levels beyond 96%. The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile CPU in real time. Finally, we propose a new generation scheme based on subscaling that folds a long sequence into a batch of shorter sequences and allows one to generate multiple samples at once. The Subscale WaveRNN produces 16 samples per step without loss of quality and offers an orthogonal method for increasing sampling efficiency.
358 citations
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TL;DR: A human--machine interface to control exoskeletons that utilizes electrical signals from the muscles of the operator as the main means of information transportation and a calibration algorithm is presented that relies exclusively on sensors mounted on the exoskeleton.
Abstract: This paper presents a human--machine interface to control exoskeletons that utilizes electrical signals from the muscles of the operator as the main means of information transportation. These signals are recorded with electrodes attached to the skin on top of selected muscles and reflect the activation of the observed muscle. They are evaluated by a sophisticated but simplified biomechanical model of the human body to derive the desired action of the operator. A support action is computed in accordance to the desired action and is executed by the exoskeleton. The biomechanical model fuses results from different biomechanical and biomedical research groups and performs a sensible simplification considering the intended application. Some of the model parameters reflect properties of the individual human operator and his or her current body state. A calibration algorithm for these parameters is presented that relies exclusively on sensors mounted on the exoskeleton. An exoskeleton for knee joint support was designed and constructed to verify the model and to investigate the interaction between operator and machine in experiments with force support during everyday movements.
358 citations
Authors
Showing all 27602 results
Name | H-index | Papers | Citations |
---|---|---|---|
Markus Antonietti | 176 | 1068 | 127235 |
Jian Li | 133 | 2863 | 87131 |
Klaus-Robert Müller | 129 | 764 | 79391 |
Michael Wagner | 124 | 351 | 54251 |
Shi Xue Dou | 122 | 2028 | 74031 |
Xinchen Wang | 120 | 349 | 65072 |
Michael S. Feld | 119 | 552 | 51968 |
Jian Liu | 117 | 2090 | 73156 |
Ary A. Hoffmann | 113 | 907 | 55354 |
Stefan Grimme | 113 | 680 | 105087 |
David M. Karl | 112 | 461 | 48702 |
Lester Packer | 112 | 751 | 63116 |
Andreas Heinz | 108 | 1078 | 45002 |
Horst Weller | 105 | 451 | 44273 |
G. Hughes | 103 | 957 | 46632 |