Re-training Deep Neural Networks to Facilitate Boolean Concept Extraction

No abstract is available for this paper.

• Publication year

  2017

• Venue

  IFIP Working Conference on Database Semantics

• Publication date

  2017-10-15

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1007/978-3-319-67786-6_10
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• DeepRED - Rule Extraction from Deep Neural Networks

  2016cited by this paper
• Learning both Weights and Connections for Efficient Neural Network

  2015cited by this paper
• BinaryConnect: Training Deep Neural Networks with binary weights during propagations

  2015cited by this paper
• Dropout: a simple way to prevent neural networks from overfitting

  2014cited by this paper
• Comprehensible classification models: a position paper

  2014cited by this paper
• Exact Rule Learning via Boolean Compressed Sensing

  2013cited by this paper
• Visualizing and Understanding Convolutional Networks

  2013cited by this paper
• Foundations of Rule Learning

  2012cited by this paper
• Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications

  2012cited by this paper
• How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations

  2009cited by this paper
• Statistical Comparisons of Classifiers over Multiple Data Sets

  2006cited by this paper
• Finding Cancer Biomarkers from Mass Spectrometry Data by Decision Lists

  2004cited by this paper
• Extracting Knowledge from Artificial Neural Networks: An Empirical Comparison of Trepan and Symbolic Learning Algorithms

  2002cited by this paper
• Rule extraction from neural networks via decision tree induction

  2001influential reference
• Multi-Valued and Universal Binary Neurons

  2000cited by this paper
• Extracting rules from trained neural networks

  2000cited by this paper
• A Penalty-Function Approach for Pruning Feedforward Neural Networks

  1997cited by this paper
• Extracting Rules from Neural Networks by Pruning and Hidden-Unit Splitting

  1997cited by this paper
• Extracting Rules from Pruned Neural Networks for Breast Cancer Diagnosis

  1996cited by this paper
• Symbolic Representation of Neural Networks

  1996cited by this paper
• Survey and critique of techniques for extracting rules from trained artificial neural networks

  1995cited by this paper
• Using Sampling and Queries to Extract Rules from Trained Neural Networks

  1994cited by this paper
• Extracting Refined Rules from Knowledge-Based Neural Networks

  1993cited by this paper
• Second Order Derivatives for Network Pruning: Optimal Brain Surgeon

  1992cited by this paper
• Improving Generalization of Neural Networks Through Pruning

  1991cited by this paper
• Rule Learning by Searching on Adapted Nets

  1991cited by this paper
• in Advances in Neural Information Processing

  1989cited by this paper
• Optimal Brain Damage

  1989cited by this paper
• Digital Logic Design

  1988cited by this paper

• An Empirical Investigation Into Deep and Shallow Rule Learning

  2021cites this paper
• Knowledge Discovery from Layered Neural Networks Based on Non-negative Task Matrix Decomposition

  2020cites this paper
• PLAY: A Profiled Linear Weighting Scheme for Understanding the Influence of Input Variables on the Output of a Deep Artificial Neural Network

  2020cites this paper
• Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation

  2020cites this paper
• UNIVERZA V LJUBLJANI FAKULTETA ZA MATEMATIKO IN FIZIKO FAKULTETA ZA RAČUNALNIŠTVO IN INFORMATIKO Računalništvo in matematika – 2. stopnja

  2020cites this paper
• Advances in Machine Learning for the Behavioral Sciences

  2019cites this paper
• Interactive Multiagent Adaptation of Individual Classification Models for Decision Support

  2019cites this paper
• Integrating Deep Learning and Bayesian Reasoning

  2019cites this paper
• Understanding Community Structure in Layered Neural Networks

  2018cites this paper
• Knowledge Discovery from Layered Neural Networks based on Non-negative Task Decomposition

  2018cites this paper