Geometric Deep Learning
Michael emphasizes the power of symmetry in geometric deep learning, deriving concepts like convolution from first principles. Tim highlights Professor Bronstein's expertise in graph representation learning, with applications in various fields. Petar's work focuses on neural network architectures for graph representation learning and computational biology, contributing popular convolutional layers for graphs.In this clip
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