A Convexity Principle for Interacting Gases: Theory
Introduces displacement interpolation to prove ground state uniqueness via optimal transport, establishing foundations …
Machine Learning Fundamentals
Neural ODEs: Continuous-Depth Deep Learning
Introduces ODE-Nets, a continuous-depth neural network model parameterized by ODEs, enabling constant memory …
Distributed Representations: A Foundational Theory
Hinton's 1984 technical report establishing the theoretical efficiency of distributed representations over local …
Mixture Density Networks: Modeling Multimodal Distributions
Seminal 1994 paper introducing MDNs to model arbitrary conditional probability distributions using neural networks.
Communication in the Presence of Noise: Shannon's 1949 Paper
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 …
5 Axes of Multi-Arm Bandit Problems: A Practical Guide
Explore 5 key dimensions of multi-arm bandit problems to help practitioners better navigate the exploration-exploitation …
A Guide to Neuroevolution: NEAT and HyperNEAT
Explore the evolution of neural network topologies with NEAT and how HyperNEAT scales this approach using geometric …
Breaking Down Machine Learning for the Average Person
Discover how machine learning actually works through three fundamental approaches, explained with everyday examples you …