Jul 12, 2020
Learning how to predict future events from patterns of past events is difficult when the set of possible event types is large. Many of the patterns detected in the data by training an everything-affects-everything model will be spurious. To exploit known structure, we propose using a deductive database to track facts over time, where each fact has a time-varying state—a vector computed by a neural net whose topology is determined by the fact’s provenance and experience. The possible events at any time correspond to structured facts, whose probabilities are modeled along with their states. In both synthetic and real-world domains, we show that neural models derived from concise Datalog programs achieve better generalization by encoding appropriate domain knowledge into the model architecture.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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