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Artificial neural networks in pattern recognition : 8th IAPR TC3 Workshop, ANNPR 2018, Siena, Italy, September 19-21, 2018, Proceedings / Luca Pancioni, Friedhelm Schwenker, Edmondo Trentin (eds.).

By: Contributor(s): Material type: TextTextSeries: Lecture notes in computer science ; 11081. | Lecture notes in computer science. Lecture notes in artificial intelligence. | LNCS sublibrary. SL 7, Artificial intelligence.Publisher: Cham : Springer, 2018Description: 1 online resource (xi, 408 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319999784
  • 3319999788
  • 9783319999791
  • 3319999796
Other title:
  • ANNPR 2018
Subject(s): Genre/Form: Additional physical formats: Print version:: Artificial neural networks in pattern recognition.DDC classification:
  • 006.3/2 23
LOC classification:
  • TK7882.P3 A56 2018eb
Online resources:
Contents:
Intro -- Preface -- Organization -- Contents -- Invited Papers -- What's Wrong with Computer Vision? -- 1 Introduction -- 2 Top Ten Questions a Theory on Vision Should Address -- 3 Hierarchical Description of Visual Tasks -- 3.1 Pixel-Wise and Abstract Visual Interpretations -- 3.2 The Interwound Story of Vision and Language -- 3.3 When Vision Collapses to Classification -- 4 Conclusions -- References -- Deep Learning in the Wild -- 1 Introduction -- 2 Face Matching -- 3 Print Media Monitoring -- 4 Visual Quality Control -- 5 Music Scanning -- 6 Game Playing -- 7 Automated Machine Learning
8 Conclusions -- References -- Learning Algorithms and Architectures -- Effect of Equality Constraints to Unconstrained Large Margin Distribution Machines -- 1 Introduction -- 2 Least Squares Support Vector Machines -- 3 Large Margin Distribution Machines and Their Variants -- 3.1 Large Margin Distribution Machines -- 3.2 Least Squares Large Margin Distribution Machines -- 3.3 Unconstrained Large Margin Distribution Machines -- 4 Performance Evaluation -- 4.1 Conditions for Experiment -- 4.2 Results for Two-Class Problems -- 5 Conclusions -- References -- DLL: A Fast Deep Neural Network Library
1 Introduction -- 2 DLL: Deep Learning Library -- 2.1 Performance -- 2.2 Example -- 3 Experimental Evaluation -- 4 MNIST -- 4.1 Fully-Connected Neural Network -- 4.2 Convolutional Neural Network -- 5 CIFAR-10 -- 6 ImageNet -- 7 Conclusion and Future Work -- References -- Selecting Features from Foreign Classes -- 1 Introduction -- 2 Methods -- 2.1 Learning from Context Classes -- 2.2 Foreign Class Combinations -- 3 Experiments -- 3.1 Datasets -- 4 Results -- 5 Discussion and Conclusion -- References -- A Refinement Algorithm for Deep Learning via Error-Driven Propagation of Target Outputs
1 Introduction -- 2 Error-Driven Target Propagation: Formalization of the Algorithms -- 2.1 The Inversion Net -- 2.2 Refinement of Deep Learning via Target Propagation -- 3 Experiments -- 4 Conclusions -- References -- Combining Deep Learning and Symbolic Processing for Extracting Knowledge from Raw Text -- 1 Introduction -- 2 Model -- 2.1 Semantic Features -- 2.2 Logic Constraints -- 2.3 Segmentation -- 3 Experiments -- 4 Conclusions -- References -- SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation -- 1 Introduction -- 2 Related Work
3 Proposed Method -- 4 Experiments -- 4.1 Network Architecture -- 4.2 Training Methodology -- 4.3 Isolated Learning -- 4.4 Adding New Tasks to the Models -- 4.5 Three Tasks Scenario -- 5 Conclusion -- References -- Classification Uncertainty of Deep Neural Networks Based on Gradient Information -- 1 Introduction -- 2 Entropy, Softmax Baseline and Gradient Metrics -- 3 Meta Classification -- A Benchmark Between Maximum Softmax Probability and Gradient Metrics -- 4 Recognition of Unlearned Concepts -- 5 Meta Classification with Known Unknowns -- 6 Conclusion and Outlook -- References
Summary: Chapter "Bounded Rational Decision-Making with Adaptive Neural Network Priors" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
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International conference proceedings.

Includes author index.

Online resource; title from PDF title page (SpringerLink, viewed September 7, 2018).

Chapter "Bounded Rational Decision-Making with Adaptive Neural Network Priors" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Intro -- Preface -- Organization -- Contents -- Invited Papers -- What's Wrong with Computer Vision? -- 1 Introduction -- 2 Top Ten Questions a Theory on Vision Should Address -- 3 Hierarchical Description of Visual Tasks -- 3.1 Pixel-Wise and Abstract Visual Interpretations -- 3.2 The Interwound Story of Vision and Language -- 3.3 When Vision Collapses to Classification -- 4 Conclusions -- References -- Deep Learning in the Wild -- 1 Introduction -- 2 Face Matching -- 3 Print Media Monitoring -- 4 Visual Quality Control -- 5 Music Scanning -- 6 Game Playing -- 7 Automated Machine Learning

8 Conclusions -- References -- Learning Algorithms and Architectures -- Effect of Equality Constraints to Unconstrained Large Margin Distribution Machines -- 1 Introduction -- 2 Least Squares Support Vector Machines -- 3 Large Margin Distribution Machines and Their Variants -- 3.1 Large Margin Distribution Machines -- 3.2 Least Squares Large Margin Distribution Machines -- 3.3 Unconstrained Large Margin Distribution Machines -- 4 Performance Evaluation -- 4.1 Conditions for Experiment -- 4.2 Results for Two-Class Problems -- 5 Conclusions -- References -- DLL: A Fast Deep Neural Network Library

1 Introduction -- 2 DLL: Deep Learning Library -- 2.1 Performance -- 2.2 Example -- 3 Experimental Evaluation -- 4 MNIST -- 4.1 Fully-Connected Neural Network -- 4.2 Convolutional Neural Network -- 5 CIFAR-10 -- 6 ImageNet -- 7 Conclusion and Future Work -- References -- Selecting Features from Foreign Classes -- 1 Introduction -- 2 Methods -- 2.1 Learning from Context Classes -- 2.2 Foreign Class Combinations -- 3 Experiments -- 3.1 Datasets -- 4 Results -- 5 Discussion and Conclusion -- References -- A Refinement Algorithm for Deep Learning via Error-Driven Propagation of Target Outputs

1 Introduction -- 2 Error-Driven Target Propagation: Formalization of the Algorithms -- 2.1 The Inversion Net -- 2.2 Refinement of Deep Learning via Target Propagation -- 3 Experiments -- 4 Conclusions -- References -- Combining Deep Learning and Symbolic Processing for Extracting Knowledge from Raw Text -- 1 Introduction -- 2 Model -- 2.1 Semantic Features -- 2.2 Logic Constraints -- 2.3 Segmentation -- 3 Experiments -- 4 Conclusions -- References -- SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation -- 1 Introduction -- 2 Related Work

3 Proposed Method -- 4 Experiments -- 4.1 Network Architecture -- 4.2 Training Methodology -- 4.3 Isolated Learning -- 4.4 Adding New Tasks to the Models -- 4.5 Three Tasks Scenario -- 5 Conclusion -- References -- Classification Uncertainty of Deep Neural Networks Based on Gradient Information -- 1 Introduction -- 2 Entropy, Softmax Baseline and Gradient Metrics -- 3 Meta Classification -- A Benchmark Between Maximum Softmax Probability and Gradient Metrics -- 4 Recognition of Unlearned Concepts -- 5 Meta Classification with Known Unknowns -- 6 Conclusion and Outlook -- References

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