A Convexity Principle for Interacting Gases
Introduces displacement interpolation to prove ground state uniqueness via optimal transport, establishing foundations …
Machine Learning Fundamentals
Neural Ordinary Differential Equations
Introduces ODE-Nets, a continuous-depth neural network model parameterized by ODEs, enabling constant memory …
Distributed Representations
Hinton's 1984 technical report establishing the theoretical efficiency of distributed representations over local …
Mixture Density Networks
Seminal 1994 paper introducing MDNs to model arbitrary conditional probability distributions using neural networks.
Communication in the Presence of Noise
Shannon's 1949 foundational paper establishing information theory, channel capacity, and the sampling theorem for …
3D Steerable CNNs: Rotationally Equivariant Features
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: 5 Key Dimensions
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 …