Communication in the Presence of Noise
Shannon's 1949 foundational paper establishing information theory, channel capacity, and the sampling theorem for …...
Machine Learning Fundamentals
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Weiler et al.'s NeurIPS 2018 paper introducing SE(3)-equivariant CNNs for volumetric data using group theory and …...
A Framework for Multi-Arm Bandit Problems
Framework for understanding multi-arm bandit algorithms through five dimensions. Covers exploration vs exploitation and …
HyperNEAT: Scaling Neuroevolution with Geometric Patterns
How HyperNEAT uses indirect encoding and geometric patterns to evolve large-scale neural networks with biological …
NEAT: Evolving Neural Network Topologies
Learn about NEAT's approach to evolving neural networks: automatic topology design, historical markings, and speciation …
Breaking Down Machine Learning for the Average Person
Discover how machine learning actually works through three fundamental approaches, explained with everyday examples you …