## IJCAI 2021 TutorialNeural Machine Reasoning |
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## Time: Aug 19 20:00 – 2:00 (Next day) Montreal Time (UTC-4) |

Current machine learning, powered by deep neural networks, excels at extracting predictive patterns from loads of data and training signals. In the past seven years, there has been a steady growth in extending this capability to the field of reasoning – the capacity to deliberately deduce new knowledge out of the existing knowledge base. This tutorial presents an organized body of knowledge that covers the recent developments around this conjunction of machine learning and reasoning with focus on differentiable neural network architectures. The main question we want to answer is whether we can learn to reason from data in the same way that we can learn to predict using neural networks? In this tutorial, we will show how this is achievable by using dynamic neural networks whose computational graphs are composed on-the-fly given data and query. Here the query is arbitrary, e.g., in linguistic form. The data and the domain have structures spanned both in space and time, i.e., data elements are interlinked by relations either implicitly or explicitly. Covered topics are organized into two parts, theory and applications. The theory part consists of dual–system account of reasoning, neural memories, reasoning over unstructured sets and over structured graphs, and neuro–symbolic integration. The applications part covers neural reasoning in machine reading comprehension, visual question answering and combinatorial inference.

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