Deep Learning for Industrial Applications

Seminar an der Universitšt des Saarlandes, Fachrichtung Informatik, LVst 109686

The topics for the seminar time slots are listed below in the order of their scheduling. Currently available topics are marked in green; already assigned topics are marked in red. Please check the seminar schedule for available time slots. The topics are in the industrial application domains Finance, Healthcare, Autonomous Driving, Recommendation; selected general background literature on the application of deep learning in these domains is indicated.

XOR: Either topic Xa or Xb has to be presented in this slot; indicate your choice (Xa or Xb) when pre-registering.

Topic#

Topic

Finance

1a/b

(a) L Zhang et al. (2017):
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns.
Proceedings 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
http://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf

-- XOR --

(b) M Schreyer et al. (2017):
Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks.
arXiv:1709.05254. https://arxiv.org/abs/1709.05254 

Healthcare

Background:

2a/b

(a) Z Yan et al. (2016):
Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition.
IEEE Trans. Med. Imag., 35(5):1332–1343.
https://pdfs.semanticscholar.org/1e89/444ca418175f77ca1c6b5ec53ac0e534f583.pdf 

-- XOR --

(b) N Tajbakhsh et al. (2016):
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?”
IEEE Trans. Med. Imag., vol. 35, no. 5, pp. 1299–1312
https://arxiv.org/pdf/1706.00712.pdf 

3

Z Che et al. (2017):
Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records.
https://arxiv.org/pdf/1709.01648 

Autonomous Driving

Background:

4

A UÁar et al. (2017):
Object Recognition and Detection with Deep Learning for Autonomous Driving Applications.
Simulation, 93(9), 759-769.
http://journals.sagepub.com/doi/pdf/10.1177/0037549717709932 

5

X Huazhe et al. (2017):
End-to-end Learning of Driving Models from Large-scale Video Datasets.
http://openaccess.thecvf.com/content_cvpr_2017/papers/Xu_End-To-End_Learning_of_CVPR_2017_paper.pdf 

6

X Wang et al. (2017):
Capturing Car-Following Behaviors by Deep Learning.
IEEE Transactions on Intelligent Transportation Systems.
DOI 10.1109/TITS.2017.2706963

7

J Zhang et al. (2016):
Deep Reinforcement Learning with Successor Features for Navigation Across Similar Environments.
arXiv:1612.05533.
https://arxiv.org/pdf/1612.05533.pdf

8

H Zou et al. (2018):
Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process.
arXiv:1801.08391.
https://arxiv.org/pdf/1801.08391.pdf

9

WC Ma et al. (2017):
Forecasting Interactive Dynamics of Pedestrians with Fictitious Play.
Proc. CVPR.
https://arxiv.org/pdf/1604.01431.pdf

Recommendation

Background:

10

C Chen et al. (2017):
Location-Aware Personalized News Recommendation With Deep Semantic Analysis. 
IEEE Access, 5:1624–1638.
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7823033

11

S Zhang et al. (2017):
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Autoencoders.
Proceedings 40th International ACM SIGIR conference on Research and Development in Information Retrieval.
https://arxiv.org/pdf/1704.00551.pdf

12

X He et al. (2017):
Neural collaborative filtering.
Proceedings 26th International Conference on World Wide Web (pp. 173-182)
https://arxiv.org/pdf/1708.05031.pdf

Selected references on deep learning: