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Classifying High-dimensional Gaussian Mixtures: Where Kernel Methods Fail & Neural Networks Succeed
Implicit regularization and benign overfitting for neural networks in high dimensions
An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn
Distance Between Gaussian Drawn High Dimensional Vectors
Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
Separating Gaussian | High Dimensional Space | MSc Big Data Analytics
The impact of data structure on learning in two-layer neural networks
Understanding Machine Learning via Exactly Solvable Stat. Phys. Models|Lenka Zdeborová EPFL,Lausanne
The Gaussian equivalence of generative models for learning with shallow neural networks
Deep Kernels
Insights on gradient-based algorithms in high-dimensional learning
Lecture 17 on kernel methods: kernels for probabilistic models