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Found 11 papers, 2 papers with code
A Decomposition-Based Hybrid Ensemble CNN Framework for Driver Fatigue Recognition
Specifically, the performance of different decomposition methods and ensemble modes was further compared.
Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces
In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used.
The Mirror Langevin Algorithm Converges with Vanishing Bias
The Mirror Langevin Diffusion (MLD) is a sampling analogue of mirror flow in continuous time, and it has nice convergence properties under log-Sobolev or Poincare inequalities relative to the Hessian metric, as shown by Chewi et al. (2020).
Sqrt(d) Dimension Dependence of Langevin Monte Carlo
This article considers the popular MCMC method of unadjusted Langevin Monte Carlo (LMC) and provides a non-asymptotic analysis of its sampling error in 2-Wasserstein distance.
A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG
Driver drowsiness is one of main factors leading to road fatalities and hazards in the transportation industry.
Mean-Square Analysis with An Application to Optimal Dimension Dependence of Langevin Monte Carlo
This bound improves the best previously known $\widetilde{\mathcal{O}}\left(\frac{d}{\epsilon}\right)$ result and is optimal in both dimension $d$ and accuracy tolerance $\epsilon$ for log-smooth and log-strongly-convex target measures.
Hessian-Free High-Resolution Nesterov Acceleration for Sampling
Nesterov's Accelerated Gradient (NAG) for optimization has better performance than its continuous time limit (noiseless kinetic Langevin) when a finite step-size is employed \citep{shi2021understanding}.
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients
In our practical implementation of EWSG, the non-uniform subsampling is performed efficiently via a Metropolis-Hastings chain on the data index, which is coupled to the MCMC algorithm.
A cost-reducing partial labeling estimator in text classification problem
We propose a new approach to address the text classification problems when learning with partial labels is beneficial.
Learning to Match via Inverse Optimal Transport
We emphasize that the discrete optimal transport plays the role of a variational principle which gives rise to an optimization-based framework for modeling the observed empirical matching data.
Efficient fetal-maternal ECG signal separation from two channel maternal abdominal ECG via diffusion-based channel selection
There is a need for affordable, widely deployable maternal-fetal ECG monitors to improve maternal and fetal health during pregnancy and delivery.
