Advances in Artificial Neural Networks

Seminar an der Universität des Saarlandes, Fachrichtung Informatik, LVst 106020

Please note that the following topic list contains more topics (24) than time slots (12) for presentation at the seminar are available: Please check the seminar schedule for available time slots.

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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.


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


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


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


Generative Adversarial Networks and Reinforcement Learning

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


Deep Reinforcement Learning (1)

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


Deep Reinforcement Learning (2)

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


Deep Reinforcement Learning (3)

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


Deep Reinforcement Learning (4)

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


Deep Reinforcement Learning (5)

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


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


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


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)


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


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)


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).


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.


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


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;


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


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)


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.


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


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.

Selected references on deep learning:

Selected references on semantic web: