KGC 2022 Keynote: 'Deep Learning with Knowledge Graphs' by Stanford's Prof. Jure Leskovec
4.9 هزار بار بازدید -
2 سال پیش
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In this keynote, Stanford University's
In this keynote, Stanford University's Professor Jure Leskovec discusses the recent methodological advancements that automatically learn to encode graph structure into low-dimensional embedding.
He also presents the industrial applications, software frameworks, benchmarks, and challenges with scaling-up graph learning systems.
Heterogeneous knowledge graphs are emerging as an abstraction to represent complex data, such as social networks, knowledge graphs, molecular graphs, and biomedical networks, as well as for modeling 3D objects, manifolds, and source code.
Machine learning, especially deep representation learning, on graphs is an emerging field with a wide array of applications from protein folding and fraud detection, to drug discovery and recommender systems.
#DeepLearning #KnowledgeGraphs #MachineLearning
He also presents the industrial applications, software frameworks, benchmarks, and challenges with scaling-up graph learning systems.
Heterogeneous knowledge graphs are emerging as an abstraction to represent complex data, such as social networks, knowledge graphs, molecular graphs, and biomedical networks, as well as for modeling 3D objects, manifolds, and source code.
Machine learning, especially deep representation learning, on graphs is an emerging field with a wide array of applications from protein folding and fraud detection, to drug discovery and recommender systems.
#DeepLearning #KnowledgeGraphs #MachineLearning
2 سال پیش
در تاریخ 1401/04/22 منتشر شده
است.
4,910
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