Optimization Geometry and Convergence
Explore PL inequalities, linear convergence rates, and the geometry of deep network optimization landscapes.
Featured Articles
Approximation, Generalization, and Scaling
Barron-space approximation theory, Rademacher bounds, and compute-optimal scaling laws for neural networks.
Diffusion Models and Normalizing Flows
Unified view of generative models, probability flow ODEs, and the mathematical foundations of diffusion models.
Higher-Order Methods for Deep Learning
Newton methods, cubic regularization, natural gradients, and practical approximations like K-FAC and Shampoo.
Hyperparameters, Schedules, and Stability
Learning rate scaling, warmup theorems, cosine decay, and stability analysis for large-scale training.
About NeuralNets.com
NeuralNets.com provides advanced, research-level content on neural network theory and practice. Our articles are written for Stanford post-graduate ML students and researchers who want to understand the mathematical foundations of deep learning.
📐
Mathematical Rigor
Formal theorems, proofs, and mathematical analysis of neural network behavior
🔬
Research Insights
Cutting-edge developments and theoretical advances in deep learning
⚡
Practical Applications
Connecting theory to practice in modern neural network systems