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Applications of GraphML like Predicting Protein Folding
Manage episode 445509124 series 3605861
A tartalmat a Brian Carter biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Brian Carter vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
Solving an impossible mystery... forget what you thought was possible!
This is a discussion of a video from Stanford's CS224W course which focuses on the many applications of graph machine learning, a field that utilizes graph data structures to solve complex problems. The speaker highlights different tasks and their associated applications, classifying them into four levels: node level, where the focus is on individual nodes; edge level, analyzing relationships between pairs of nodes; subgraph level, examining groups of nodes; and graph level, analyzing the entire graph structure. The lecture provides a detailed overview of various applications in diverse fields, including protein folding, recommender systems, drug discovery, traffic prediction, and physics-based simulations.
Watch the video: https://www.youtube.com/watch?v=aBHC6xzx9YI
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