Abstract
Deep learning is the machine learning method that changed the field of artificial intelligence in the last five years. In the view of industrial research, this technology is disruptive: It considerably pushes the border of tasks that can be automated, changes the way applications are developed, and is available to virtually everyone.
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References
AnnotateMyData. http://annotatemydata.com/. Accessed 28 Feb 2018
Angelova, A., et al.: Real-time pedestrian detection with deep network cascades. In: British Machine Vision Conference (BMVC), pp. 32.1–32.12 (2015)
Bahdanau, D., et al.: Neural machine translation by jointly learning to align and translate. In: 3rd International Conference on Learning Representations (ICLR) (2015)
Banko, M., Brill, E.: Scaling to very very large corpora for natural language disambiguation. In: 39th Annual Meeting ot the Association for Computational Linguistics (ACL), pp. 26–33 (2001)
Behrendt, K., et al.: A deep learning approach to traffic lights: detection, tracking, and classification. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1370–1377 (2017)
Bellman, R.: A Markovian decision process. J. Math. Mech. 6(5), 679–684 (1954)
Bojarski, M., et al. Explaining how a deep neural network trained with end-to-end learning steers a car. Computing Research Repository, arXiv:1704.07911 (2017)
Boston Dynamics: Atlas Robot. https://www.bostondynamics.com/atlas. Accessed 28 Feb 2018
Cheng, J., et al.: Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci. Rep. 6(24454) (2016)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734 (2014)
Clevert, D. et al.: Fast and accurate deep network learning by exponential linear units (ELUs). In: 4th International. Conference on Learning Representations (ICLR) (2016)
Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3213–3223 (2016)
CrowdFlower. https://www.crowdflower.com/. Accessed 28 Feb 2018
Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control, Signals, Syst. 2(4), 303–314 (1989)
Dai, J., et al.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems (NIPS), vol. 29, pp. 379–387 (2016)
DiGiovanna, J., et al.: Coadaptive brain-machine interface via reinforcement learning. IEEE Trans. Biomed. Eng. 56(1), 54–64 (2009)
Doersch, C., et al.: Unsupervised visual representation learning by context prediction. In: IEEE International Conference on Computer Vision (ICCV), pp. 1422–1430 (2015)
Dong, C., et al.: Learning a deep convolutional network for image super-resolution. In: 13th European Conference on Computer Vision (ECCV), pp. 184–199 (2014)
Duchi, J., et al.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)
Espinosa, J., et al.: Vehicle detection using AlexNet and Faster R-CNN deep learning models: a comparative study. In: 5th International Visual Informatics Conference (IVIC), pp. 3–15 (2017)
Farfade, S., et al.: Multi-view face detection using deep convolutional neural networks. In: 5th ACM on International Conference on Multimedia Retrieval (ICMR), pp. 643–650 (2015)
Géron, A.: Hands-On Machine Learning with Scikit-Learn and Tensor-Flow. O’Reilly, Sebastopol (2017)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: 13th International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)
Glorot, X., et al.: Deep sparse rectifier neural networks. In: 14th International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems 27 (NIPS), pp. 2672–2680 (2014)
Goodfellow, I., et al.: Deep Learning. MIT Press, Cambridge (2016)
Google, Inc.: Neural network processor. Patent WO2016186801 (2016)
Greff, K., et al.: LSTM: a search space odyssey. IEEE Trans. Neural Networks Learn. Syst 28(10), 2222–2232 (2017)
He, K. et al.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: IEEE International Conference on Computer Vision (ICCV), pp. 1026–1034, 2015
He, K., et al.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
He, K., et al.: Mask R-CNN. In: IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988 (2017)
Hinton, G., et al.: A fast learning algorithm for deep belief nets. Neural Comput 18, 1527–1554 (2006)
Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82–97 (2012)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Hornik, K., et al.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)
Huang, J., et al.: Speed/accuracy trade-offs for modern convolutional object detectors. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3296–3297 (2017)
Hubel, D., Wiesel, T.: Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148(3), 574–591 (1959)
Intel Nervana. https://ai.intel.com/. Accessed 28 Feb 2018
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on International Conference on Machine Learning (ICML), pp. 448–456 (2015)
Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 664–676 (2017)
Kendall, A., et al.: End-to-end learning of geometry and context for deep stereo regression. In: IEEE Int. Conference on ComputerVision (ICCV), pp. 66–75 (2017)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (ICLR) (2015)
Krizhevsky, A. et al.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25 (NIPS), pp. 1090–1098 (2012)
Larsson, G., et al.: Colorization as a proxy task for visual understanding. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 840–849 (2017)
Le, Q., et al.: A simple way to initialize recurrent networks of rectified linear units. Computing Research Repository, abs/1504.00941 (2015)
LeCun, Y., et al.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems 2 (NIPS), pp. 396–404 (1990)
LeCun, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
LeCun, Y., et al.: Deep learning. Nature 521(7553), 436–444 (2015)
Levine, S., et al.: End-to-end training of deep visuomotor policies. J. Mach. Learn. Res. 17(1), 1334–1373 (2016)
Levine, S., et al.: Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. Int. J. Robo. Res. 37(4) (2017)
Li, Y., et al.: Fully convolutional instance-aware semantic segmentation. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4438–4446 (2017)
Littman, M.: Reinforcement learning improves behaviour from evaluative feedback. Nature 521(7553), 445–451 (2015)
Liu, W., et al.: SSD: single shot multibox detector. In: 14th European Conference on Computer Vision (ECCV), pp. 396–404 (2016)
Long, J., et al.: Fully convolutional networks for semantic segmentation. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015)
Luong, M., et al.: Effective approaches to attention-based neural machine translation. Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1412–1421 (2015)
Matti, D., et al.: Combining LiDAR space clustering and convolutional neural networks for pedestrian detection. In: 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017)
McCulloch, W., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)
Mnih, V., et al.: Playing Atari with deep reinforcement learning. NIPS Deep Learning Workshop (2013)
Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)
Nesterov, Y.: A method of solving a convex programming problem with convergence rate O(1/k2). Sov. Math. Dokl 27(2), 372–376 (1983)
NVIDIA CUDA. https://developer.nvidia.com/cuda. Accessed 28 Feb 2018
NVIDIA cuDNN. https://developer.nvidia.com/cudnn. Accessed 28 Feb 2018
NVIDIA Jetson. https://developer.nvidia.com/embedded-computing. Accessed 28 Feb 2018
NVIDIA Drive. https://developer.nvidia.com/drive. Accessed 28 Feb 2018
Pathak, D. et al.: Context encoders: feature learning by inpainting. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2536–2544 (2016)
Peters, J., Schaal, S.: Reinforcement learning of motor skills with policy gradients. Neural Netw. 21(4), 682–697 (2008)
Pham, V., et al.: Dropout improves recurrent neural networks for handwriting recognition. In: 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 285–290 (2014)
Polyak, B.: Some methods of speeding up the convergence of iteration methods. USSR Comput. Math. Math. Phys. 4(5), 1–17 (1964)
Redmon, J., et al.: You only look once: unified, real-time object detection. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems 28 (NIPS), pp. 91–99 (2015)
Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386–408 (1958)
Rumelhart, D., et al.: Learning representations by back-propagating errors. Nature 323,533–536 (1986)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Sak, H., et al.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: 15th Annual Conference of the International Speech Communication Association (INTERSPEECH), pp. 338–342 (2014)
Salimans, T., et al.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems 29 (NIPS), pp. 2234–2242 (2016)
Schulman, J., et al.: Trust region policy optimization. In: 32nd International Conference on International Conference on Machine Learning (ICML), pp. 1889–1897 (2015)
See, A., et al.: Get to the point: summarization with pointer-generator networks. In: 55th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 1073–1083 (2017)
Silver, D., et al.: Deterministic policy gradient algorithms. In: 31st International Conference on International Conference on Machine Learning (ICML), pp. 387–395 (2014)
Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations (ICLR) (2015)
Srivastava, N., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res 15, 1929–1958 (2014)
Su, H., et al.: Crowdsourcing annotations for visual object detection. In: AAAI Human Computation Workshop, pp. 40–46 (2012)
Sutskever, I., et al.: Sequence to sequence learning with neural networks. In: Neural Information Processing Systems 27 (NIPS), pp. 3104–3112 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015)
Szegedy, C., et al.: Rethinking the inception architecture for computer vision. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)
Szegedy, C., et al.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: 31st AAAI Conference on Artificial Intelligence, pp. 4278–4284 (2017)
Tesauro, G.: Temporal difference learning and TD-Gammon. Commun. ACM 38(3), 58–68 (1995)
Waymo: Google self-driving car. https://waymo.com/. Accessed 28 Feb 2018
Werbos, P.: Beyond regression: new tools for prediction and analysis in the behavioral sciences. PhD thesis, Harvard University (1974)
Werbos, P.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550–1560 (1990)
Williams, R.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8(3), 229–256 (1992)
Wilson, A., et al.: The marginal value of adaptive gradient methods in machine learning. In: Advances in Neural Information Processing Systems 30 (NIPS), pp. 4151–4161 (2017)
Xu, B., et al.: Empirical evaluation of rectified activations in convolutional network. In: ICML Deep Learning Workshop, 06–11 July 2015
Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: 32nd International Conference on International Conference on Machine Learning (ICML), pp. 2048–2057 (2015)
Zeiler M., Fergus, R.: Visualizing and understanding convolutional networks. In: 13th European Conference on Computer Vision (ECCV), pp. 818–833 (2014)
Zhang, Y., et al.: Augmenting supervised neural networks with unsupervised objectives for large-scale image classification. In: 33rd International Conference on International Conference on Machine Learning (ICML), pp. 612–621 (2016)
Zhu, Z., et al.: Traffic sign detection and classification in the wild. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2110–2118 (2016)
Zoph, B., Le, Q.: Neural architecture search with reinforcement learning. In: 5th International Conference on Learning Representations (ICLR) (2017)
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Vogt, M. (2019). An Overview of Deep Learning and Its Applications. In: Bertram, T. (eds) Fahrerassistenzsysteme 2018. Proceedings. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-23751-6_17
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