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Many knowledge bases have constituted large collections of multi-relational data, provides machines with a shared understanding of human knowledge in different domains. In the scenario of machine learning, there are two critical problems related to multi-relational data. One is to develop effective representation learning approaches transform such relational knowledge into quantifiable and predictable latent representations. This process is key to the support of many knowledge-driven applications. The other is to effectively extract relational knowledge from other modalities of data, such as text and other forms of sequence data.

Date(s) - Mar 19, 2019
3:30 pm - 5:30 pm

Engineering VI – Room 289
404 Westwood Plaza, Los Angeles California 90095