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Found 9 papers, 1 papers with code
Optimal minimax rate of learning interaction kernels
Our tLSE method offers a straightforward approach for establishing the optimal minimax rate for models with either local or nonlocal dependency.
Droplets of Good Representations: Grokking as a First Order Phase Transition in Two Layer Networks
A key property of deep neural networks (DNNs) is their ability to learn new features during training.
Hitting the High-Dimensional Notes: An ODE for SGD learning dynamics on GLMs and multi-index models
In addition to the deterministic equivalent, we introduce an SDE with a simplified diffusion coefficient (homogenized SGD) which allows us to analyze the dynamics of general statistics of SGD iterates.
Speed Limits for Deep Learning
State-of-the-art neural networks require extreme computational power to train.
Spectral-Bias and Kernel-Task Alignment in Physically Informed Neural Networks
Physically informed neural networks (PINNs) are a promising emerging method for solving differential equations.
Separation of Scales and a Thermodynamic Description of Feature Learning in Some CNNs
Deep neural networks (DNNs) are powerful tools for compressing and distilling information.
Lower Bounds on the Generalization Error of Nonlinear Learning Models
We study in this paper lower bounds for the generalization error of models derived from multi-layer neural networks, in the regime where the size of the layers is commensurate with the number of samples in the training data.
Directed polymers on infinite graphs
We study the directed polymer model for general graphs (beyond $\mathbb Z^d$) and random walks.
Probability
Multi-Season Analysis Reveals the Spatial Structure of Disease Spread
Understanding the dynamics of infectious disease spread in a heterogeneous population is an important factor in designing control strategies.
