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Found 7 papers, 1 papers with code
FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning
First, we design an inexactness criterion for the clients' local updates to eliminate the need for empirically setting the local training accuracy.
Interplay between depth and width for interpolation in neural ODEs
Our findings reveal a balancing trade-off between $p$ and $L$, with $L$ scaling as $O(1+N/p)$ for dataset interpolation, and $L=O\left(1+(p\varepsilon^d)^{-1}\right)$ for measure interpolation.
Optimized classification with neural ODEs via separability
Classification of $N$ points becomes a simultaneous control problem when viewed through the lens of neural ordinary differential equations (neural ODEs), which represent the time-continuous limit of residual networks.
Approximate and Weighted Data Reconstruction Attack in Federated Learning
Federated Learning (FL) is a distributed learning paradigm that enables multiple clients to collaborate on building a machine learning model without sharing their private data.
Turnpike in optimal control of PDEs, ResNets, and beyond
The \emph{turnpike property} in contemporary macroeconomics asserts that if an economic planner seeks to move an economy from one level of capital to another, then the most efficient path, as long as the planner has enough time, is to rapidly move stock to a level close to the optimal stationary or constant path, then allow for capital to develop along that path until the desired term is nearly reached, at which point the stock ought to be moved to the final target.
Sidewise control of 1-d waves
We analyze the sidewise controllability for the variable coefficients one-dimensional wave equation.
Optimization and Control
Large-time asymptotics in deep learning
We consider the neural ODE perspective of supervised learning and study the impact of the final time $T$ (which may indicate the depth of a corresponding ResNet) in training.
