Weiler et al.’s NeurIPS 2018 paper introducing 3D Steerable CNNs that achieve SE(3) equivariance through group representation theory and spherical harmonic convolution kernels, eliminating the need for rotational data augmentation and improving data efficiency for scientific applications with rotational symmetry like molecular and protein structures.
Key dimensions that have helped me understand multi-arm bandit problems: action space, problem structure, external information, reward mechanism, and learner feedback.
Discover how NEAT and HyperNEAT changed neuroevolution by automatically designing neural network architectures and scaling them through geometric patterns.
Understand the pattern recognition behind Netflix recommendations, email spam filters, and game-playing AI through three core machine learning approaches.
Explore the building blocks of classic AI reasoning, from knowledge bases and logic to how systems draw new conclusions from existing knowledge.