Sample Size Determination: Likelihood Bootstrapping
Presenting two approaches to determining a sufficient sample size based on likelihood values from resampled subsets
Unraveling the Hessian: A Key to Smooth Convergence in Loss Function Landscapes
Exploring the convergence of the loss landscape in neural networks as the sample size increases, focusing on the Hessian matrix to understand the local geometry of the loss function
Decentralized Optimization with Coupled Constraints
The method proposed is the first linearly convergent first-order decentralized algorithm for problems with general affine coupled constraints
Sample Size Determination: Posterior Distributions Proximity
Presenting two approaches to determining a sufficient sample size based on the proximity of posterior distributions of model parameters on similar subsets
Forecasting fMRI Images From Video Sequences: Linear Model Analysis
Exploring the correlation between video sequences and fMRI images, using a linear autoregressive model