Case-Based Reasoning and Deep Learning Workshop: CBRDL 2019

Recent advances in deep learning (DL) have helped to usher in a new wave of confidence in the capability of artificial intelligence. Increasingly, we are seeing DL architectures outperform long established state-of-the-art algorithms in a number of diverse tasks. In fact, DL has reached a point where it currently rivals or has surpassed human performance in a number of challenges e.g. image classification, speech recognition and game play.

These successes of DL call for novel methods and techniques that exploit DL for the benefit of CBR systems. In particular, the potential of DL for CBR include improvement in knowledge aggregation and feature extraction for case representation, efficient indexing and retrieval architectures as well as assisting with case adaptation.

Workshop Topics

Topics will include, but not be limited to, the following:

  • Learning Theory
  • Representation Learning
  • Deep Learning Architectures
  • Hybrid Systems
  • Deep Reinforcement Learning
  • Deep Belief Networks
  • Auto-encoders
  • Feed-Forward Neural networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Generative Adversarial Networks
  • Transfer Learning and Domain Adaptation
  • Similarity/Metric Learning Models

News

  • 24th March: web site live
  • 11th March: 3rd CBRDL Workshop is accepted