Learning and reasoning are two fundamental intelligence abilities. While machine learning has witnessed record-breaking successes in the last decade with the rapidly developed deep learning techniques, neural network models are nevertheless perceived as black-boxes. There has been a compelling need to interpret and visualize the learned representations and decisions made by NN models, especially for sensitive applications such as medical diagnosis or autonomous driving in which rare mistakes can be costly or fatal. Moreover, the ability to assemble trainable networks and thus combine previously acquired knowledge plays an extremely import role in constructing reasoning systems.
(NEW) Accepted Papers:
Venue and Registration:
The workshop will take place at Stockholmsmässan, Stockholm, Sweden. Please consult the main IJCAI-ECAI website for details on registration.