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:

• G Litjens et al. (2017): A Survey on Deep Learning in Medical Image Analysis.https://arxiv.org/pdf/1702.05747
• BP Shickel et al. (2017): Deep EHR: A Survey of Recent Advances on Deep Learning Techniques for Electronic Health Record (EHR) Analysis. https://arxiv.org/pdf/1706.03446

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:

• W Schwarting et al. (2018): Planning and Decision-Making for Autonomous Vehicles.
  Annual Review of Control, Robotics, and Autonomous Systems.
  http://www.annualreviews.org/doi/pdf/10.1146/annurev-control-060117-105157

4

A Uçar 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:

• S Zhang et al. (2017): Deep Learning Based Recommender System: A Survey and New Perspectives.
  arXiv:1707.07435. https://arxiv.org/pdf/1707.07435.pdf 

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