While early work on knowledge representation and inference was primarily symbolic, the corresponding approaches subsequently fell out of favor, and were largely supplanted by connectionist methods. In this workshop, we will work to close the gap between the two paradigms, and aim to formulate a new unified approach that is inspired by our current understanding of human cognitive processing. This is important to help improve our understanding of Neural Information Processing and build better Machine Learning systems, including the reuse of knowledge learned in one application domain in analogous domains.

The workshop brings together world leaders in the fields of neural computation, logic and artificial intelligence, natural language understanding, cognitive science, and computational neuroscience. Over the two workshop days, their invited lectures will be complemented with presentations based on contributed papers and poster sessions, giving ample opportunity to interact and discuss the different perspectives and emerging approaches.

The workshop targets a single broad theme of general interest to the vast majority of the NIPS community, namely the study of translations and ways of integration between neural models and knowledge representation for the purpose of achieving an effective integration of learning and reasoning. Neural-symbolic computing is now an established topic of wider interest to NIPS with topics that are relevant to almost everyone studying neural information processing.

Some of the relevant keywords characterizing the event are: neural-symbolic computing; language processing; cognitive agents; multimodal learning; deep networks; symbol manipulation; variable binding; integration of learning and reasoning.