Topic#

Topic

1

Recurrent Neural Networks

• W. Min et al.  (2017): Deep LSTM-based Goal Recognition Models for Open-World Digital Games. Proc. of 10th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment.

2

Recursive Neural Networks

• F. Bisson, H. Larochelle, F. Kabanza (2015): Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition. Proc. of Intern. Joint Conference on Artificial Intelligence

3

Generative Adversarial Networks (1)

• S. Reed et al. (2016): Generative Adversarial Text to Image Synthesis. Proc. of 33rd International Conference on Machine Learning, Vol. 3
• A. Radford et al. (2016): Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv:1511.06434

4

Generative Adversarial Networks (2)

• X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, P. Abbeel (2016): InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. arXiv:1606.03657

5

Generative Adversarial Networks and Reinforcement Learning

• D. Pfau, O. Vinyals (2017): Connecting Generative Adversarial Networks and Actor-Critic Methods. arXiv:1610.01945v3

6

Deep Reinforcement Learning (1)

• V. Mnih et al. (2016): Asynchronous Methods for Deep Reinforcement Learning. Proc. of 33rd International Conference on Machine Learning (ICML). 

7

Deep Reinforcement Learning (2)

• T.P. Lillicrap et al. (2016): Continuous Control With Deep Reinforcement Learning. Proc. of   International Conference on Learning Representations (ICLR) 

8

Deep Reinforcement Learning (3)

• M. Hausknecht, P. Stone (2017): Deep Recurrent Q-learning for Partially Observable MDPs. arXiv:1507.06527

9

Deep Reinforcement Learning (4)

• D. Silver et al. (2016): Mastering the Game of Go with Deep Neural Networks and Tree search. Nature, 529(7587)

10

Deep Reinforcement Learning (5)

• M. Jaderberg et al. (2016): Reinforcement Learning with Unsupervised Auxiliary Tasks. arXiv:1611.05397v1

11

Domain Adaptation (1)

• S. Ben-David et al. (2010): A Theory of Learning from Different Domains. Machine Learning, 79(1-2)
• K. Zhang, M. Gong, B. Schölkopf (2015): Multi-Source Domain Adaptation: A Causal View.  Proc of AAAI Conference

12

Domain Adaptation (2)

• K. Bousmalis et al. (2016): Domain Separation Networks. Proc. of Neural Information Processing Systems (NIPS)
• K. Bousmalis et al. (2016): Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks. arXiv:1612.05424

13

Domain Adaptation (3)

• E. Tzeng, J. Hoffman, T. Darrell, K. Saenko (2015). Simultaneous Deep Transfer Across Domains and Tasks. Proc. of IEEE International Conference on Computer Vision (ICCV)

14

Convolutional Neural Networks

• Multi-Task Learning
• Y. Kao, R. He, K. Huang (2016): Deep Aesthetic Quality Assessment with Semantic Information. IEEE Transactions on Image Processing. arXiv:1604.04970v3

15

Deep Learning for Image Analysis (1)

• V. Badrinarayanan, A. Kendall, R. Cipolla (2017): SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence
• K. He et al. (2016): Deep Residual Learning for Image Recognition. Proc. of IEEE Conference on Computer Vision and Pattern Recognition (ICVPR)

16

Deep Learning for Image Analysis (2)

• J. Long, E. Shelhamer, T. Darrell (2015): Fully Convolutional Networks for Semantic Segmentation. Proc. of IEEE Conference on Computer Vision and Pattern Recognition (ICVPR).
• J. Dai et al. (2016): R-FCN: Object Detection via Region-Based Fully Convolutional Networks. Proc. of Advances in neural information processing systems (NIPS).

17

Deep Learning for Image Analysis (3)

• F. Visin et al. (2016): Reseg: A Recurrent Neural Network-based Model for Semantic Segmentation. Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
• F. Visin et al. (2015): Renet: A Recurrent Neural Network based Alternative to Convolutional Networks. arxiv.org/abs/1505.00393

18

Deep Learning for Image Analysis (4)

• I. Bogun, A. Angelova, N. Jaitly (2015): Object Recognition From Short Videos for Robotic Perception. arXiv 1509.01602
• X. Li et al. (2017): Accurate Object Detection Using Memory-Based Models in Surveillance Scenes. Pattern Recognition, 67

19

Deep Learning for Image Analysis (5)

• M.S. Pavel, H. Schulz, S. Behnke (2017): Object Class Segmentation of RGB-D Video Using Recurrent Convolutional Neural Networks." Neural Networks;
  www.ais.uni-bonn.de/papers/Neural_Networks_2017_Pavel.pdf

20

Deep Learning for Image Analysis (6)

• P.O. Pinheiro, R. Collobert, P. Dollar (2015): Learning to Segment Object Candidates,” Advances in Neural Information Processing Systems
• P.O. Pinheiro et al. (2016): Learning to Refine Object Segments. Proc. European Conference on Computer Vision

21

Semantic Deep Learning (1)

• L. Ferrone, F.M. Zanzotto (2017): Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey. arXiv:1702.00764.
• X. Liu et al. (2015): Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval. Proc. of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (HLT-NAACL)

22

Semantic Deep Learning (2)

• H. Wang, D. Dou, D. Lowd (2016): Ontology-Based Deep Restricted Boltzmann Machine. Proc.  of International Conference on Database and Expert Systems Applications (DEXA)
• N. Phan et al. (2015): Ontology-Based Deep Learning for Human Behavior Prediction in Health Social Networks. Information Sciences, 384.
   

23

Semantic Deep Learning (3)

• F. Kummert et al. (1998): Hybrid object recognition in image sequences. Proc. of 14th IEEE International Conference on Pattern Recognition, IEEE
• G. Heidemann, F. Kummert, H. Ritter, G Sagerer (1996): A Hybrid Object Recognition Architecture. Proc. of International Conference on Neural Networks (ICANN), LNCS 1112
   

24

Semantic Deep Learning (4)

• N.E. Maillot, M. Thonnat (2008): Ontology-based Complex Object Recognition. Image and Vision Computing, 26(1):102-113.
• J.P. Schober, T. Hermes, O. Herzog, O. (2004): Content-based Image Retrieval by Ontology-based Object Recognition. Proc. of KI Workshop on Applications of Description Logics (ADL), Springer.