• 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,