Hongseok Yang (양홍석), DAG-symmetries and Symmetry-Preserving Neural Networks

Room B232 IBS (기초과학연구원)

The preservation of symmetry is one of the key tools for designing data-efficient neural networks. A representative example is convolutional neural networks (CNNs); they preserve translation symmetries, and this symmetry preservation is often attributed to their success in real-world applications. In the machine-learning community, there is a growing body of work that explores a new

Hongseok Yang (양홍석), Learning Symmetric Rules with SATNet

Room B332 IBS (기초과학연구원)

SATNet is a differentiable constraint solver with a custom backpropagation algorithm, which can be used as a layer in a deep-learning system. It is a promising proposal for bridging deep learning and logical reasoning. In fact, SATNet has been successfully applied to learn, among others, the rules of a complex logical puzzle, such as Sudoku,

IBS 이산수학그룹 Discrete Mathematics Group
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IBS Discrete Mathematics Group (DIMAG)
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