Neural Information Processing

@inproceedings{Arik2015NeuralIP,
  title={Neural Information Processing},
  author={S. Arik and Tingwen Huang and W. K. Lai and Qingshan Liu},
  booktitle={Lecture Notes in Computer Science},
  year={2015}
}
This paper explores the possibility of combining an actor and critic in one architecture and uses a mixture of updates to train them. It describes a model for robot navigation that uses architecture similar to an actor-critic rein‐ forcement learning architecture. It sets up the actor as a layer seconded by another layer which deduce the value function. Therefore, the effect is to have similar to a critic outcome combined with the actor in one network. The model hence can be used as the base… CONTINUE READING
BETA
3
Twitter Mentions

Citations

Publications citing this paper.
SHOWING 1-10 OF 14 CITATIONS

LTI ODE-valued neural networks

VIEW 1 EXCERPT
CITES METHODS

References

Publications referenced by this paper.
SHOWING 1-10 OF 50 REFERENCES

3D facial geometric features for constrained local model

  • 2014 IEEE International Conference on Image Processing (ICIP)
  • 2014
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

Incremental Face Alignment in the Wild

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition
  • 2014
VIEW 15 EXCERPTS
HIGHLY INFLUENTIAL

300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge

  • 2013 IEEE International Conference on Computer Vision Workshops
  • 2013
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

A Semi-automatic Methodology for Facial Landmark Annotation

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops
  • 2013
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

Robust Face Landmark Estimation under Occlusion

  • 2013 IEEE International Conference on Computer Vision
  • 2013
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

3D Constrained Local Model for rigid and non-rigid facial tracking

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition
  • 2012
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

Relaxed collaborative representation for pattern classification

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition
  • 2012
VIEW 8 EXCERPTS
HIGHLY INFLUENTIAL

Sparse representation or collaborative representation: Which helps face recognition?

  • 2011 International Conference on Computer Vision
  • 2011
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Similar Papers