publication . Preprint . 2018

A Survey on Deep Learning Toolkits and Libraries for Intelligent User Interfaces

Zacharias, Jan; Barz, Michael; Sonntag, Daniel;
Open Access English
  • Published: 13 Mar 2018
Abstract
This paper provides an overview of prominent deep learning toolkits and, in particular, reports on recent publications that contributed open source software for implementing tasks that are common in intelligent user interfaces (IUI). We provide a scientific reference for researchers and software engineers who plan to utilise deep learning techniques within their IUI research and development projects.
Subjects
free text keywords: Computer Science - Human-Computer Interaction, Computer Science - Learning, H.5.2
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65 references, page 1 of 5

1. Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Rafal Jozefowicz, Yangqing Jia, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Mike Schuster, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. (2015). https://www.tensorflow.org/

3. Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 2014. Power to the People: The Role of Humans in Interactive Machine Learning. AI Magazine 35, 4 (dec 2014), 105. DOI: http://dx.doi.org/10.1609/aimag.v35i4.2513 [OpenAIRE]

4. Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, and Mohak Shah. 2016. Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning. (2016), 1-11.

5. Michael Barz and Daniel Sonntag. 2016. Gaze-guided object classification using deep neural networks for attention-based computing. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct - UbiComp '16. ACM Press, New York, New York, USA, 253-256. DOI: http://dx.doi.org/10.1145/2968219.2971389

6. Gedas Bertasius, Hyun Soo Park, Stella X. Yu, and Jianbo Shi. 2017a. First Person Action-Object Detection with EgoNet. In Proceedings of Robotics: Science and Systems. http://arxiv.org/abs/1603.04908 [OpenAIRE]

7. Gedas Bertasius, Hyun Soo Park, Stella X. Yu, and Jianbo Shi. 2017b. Unsupervised Learning of Important Objects from First-Person Videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1956-1964. DOI: http://dx.doi.org/10.1109/ICCV.2017.216

8. Tom Bocklisch, Joey Faulkner, Nick Pawlowski, and Alan Nichol. 2017. Rasa: Open Source Language Understanding and Dialogue Management. (dec 2017). http://arxiv.org/abs/1712.05181 [OpenAIRE]

9. Daniel Braun and Manfred Langen. 2017. Evaluating Natural Language Understanding Services for Conversational Question Answering Systems. August (2017), 174-185.

10. John Canny and Huasha Zhao. 2013. Big Data Analytics with Small Footprint : Squaring the Cloud. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM (2013), 95-103. DOI: http://dx.doi.org/10.1145/2487575.2487677

11. Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014. Return of the Devil in the Details: Delving Deep into Convolutional Nets. British Machine Vision Conference (2014). DOI:

http://dx.doi.org/10.5244/C.28.6

12. Danqi Chen and Christopher Manning. 2014. A Fast and Accurate Dependency Parser using Neural Networks. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) i (2014), 740-750. DOI: http://dx.doi.org/10.3115/v1/D14-1082 [OpenAIRE]

13. Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. (2015), 1-6. DOI: http://dx.doi.org/10.1145/2532637

14. François Chollet and Others. 2015. Keras. (2015). https://github.com/fchollet/keras

15. Matteo Cognolato, Mara Graziani, Francesca Giordaniello, Gianluca Saetta, Franco Bassetto, Peter Brugger, Barbara Caputo, Henning Müller, and Manfredo Atzori. 2017. Semi-automatic training of an object recognition system in scene camera data using gaze tracking and accelerometers. In International Conference on Computer Vision Systems (ICVS).

65 references, page 1 of 5
Abstract
This paper provides an overview of prominent deep learning toolkits and, in particular, reports on recent publications that contributed open source software for implementing tasks that are common in intelligent user interfaces (IUI). We provide a scientific reference for researchers and software engineers who plan to utilise deep learning techniques within their IUI research and development projects.
Subjects
free text keywords: Computer Science - Human-Computer Interaction, Computer Science - Learning, H.5.2
Download from
65 references, page 1 of 5

1. Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Rafal Jozefowicz, Yangqing Jia, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Mike Schuster, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. (2015). https://www.tensorflow.org/

3. Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 2014. Power to the People: The Role of Humans in Interactive Machine Learning. AI Magazine 35, 4 (dec 2014), 105. DOI: http://dx.doi.org/10.1609/aimag.v35i4.2513 [OpenAIRE]

4. Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, and Mohak Shah. 2016. Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning. (2016), 1-11.

5. Michael Barz and Daniel Sonntag. 2016. Gaze-guided object classification using deep neural networks for attention-based computing. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct - UbiComp '16. ACM Press, New York, New York, USA, 253-256. DOI: http://dx.doi.org/10.1145/2968219.2971389

6. Gedas Bertasius, Hyun Soo Park, Stella X. Yu, and Jianbo Shi. 2017a. First Person Action-Object Detection with EgoNet. In Proceedings of Robotics: Science and Systems. http://arxiv.org/abs/1603.04908 [OpenAIRE]

7. Gedas Bertasius, Hyun Soo Park, Stella X. Yu, and Jianbo Shi. 2017b. Unsupervised Learning of Important Objects from First-Person Videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1956-1964. DOI: http://dx.doi.org/10.1109/ICCV.2017.216

8. Tom Bocklisch, Joey Faulkner, Nick Pawlowski, and Alan Nichol. 2017. Rasa: Open Source Language Understanding and Dialogue Management. (dec 2017). http://arxiv.org/abs/1712.05181 [OpenAIRE]

9. Daniel Braun and Manfred Langen. 2017. Evaluating Natural Language Understanding Services for Conversational Question Answering Systems. August (2017), 174-185.

10. John Canny and Huasha Zhao. 2013. Big Data Analytics with Small Footprint : Squaring the Cloud. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM (2013), 95-103. DOI: http://dx.doi.org/10.1145/2487575.2487677

11. Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014. Return of the Devil in the Details: Delving Deep into Convolutional Nets. British Machine Vision Conference (2014). DOI:

http://dx.doi.org/10.5244/C.28.6

12. Danqi Chen and Christopher Manning. 2014. A Fast and Accurate Dependency Parser using Neural Networks. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) i (2014), 740-750. DOI: http://dx.doi.org/10.3115/v1/D14-1082 [OpenAIRE]

13. Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. (2015), 1-6. DOI: http://dx.doi.org/10.1145/2532637

14. François Chollet and Others. 2015. Keras. (2015). https://github.com/fchollet/keras

15. Matteo Cognolato, Mara Graziani, Francesca Giordaniello, Gianluca Saetta, Franco Bassetto, Peter Brugger, Barbara Caputo, Henning Müller, and Manfredo Atzori. 2017. Semi-automatic training of an object recognition system in scene camera data using gaze tracking and accelerometers. In International Conference on Computer Vision Systems (ICVS).

65 references, page 1 of 5
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