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Found 9 papers, 1 papers with code
A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network
A&B BNN is proposed to directly remove part of the multiplication operations in a traditional BNN and replace the rest with an equal number of bit operations, introducing the mask layer and the quantized RPReLU structure based on the normalizer-free network architecture.
Multivariate Bayesian Structural Time Series Model
This paper deals with inference and prediction for multiple correlated time series, where one has also the choice of using a candidate pool of contemporaneous predictors for each target series.
The non-tightness of the reconstruction threshold of a 4 states symmetric model with different in-block and out-block mutations
The tree reconstruction problem is to collect and analyze massive data at the $n$th level of the tree, to identify whether there is non-vanishing information of the root, as $n$ goes to infinity.
Facial Makeup Transfer Combining Illumination Transfer
To meet the women appearance needs, we present a novel virtual experience approach of facial makeup transfer, developed into windows platform application software.
Bayesian Feature Selection in Joint Quantile Time Series Analysis
Feature selection is conducted in the quantile regression component, where each time series has its own pool of contemporaneous external predictors allowing nowcasting.
Spike and slab Bayesian sparse principal component analysis
Sparse principal component analysis (SPCA) is a popular tool for dimensionality reduction in high-dimensional data.
The mbsts package: Multivariate Bayesian Structural Time Series Models in R
The multivariate Bayesian structural time series (MBSTS) model is a general machine learning model that deals with inference and prediction for multiple correlated time series, where one also has the choice of using a different candidate pool of contemporaneous predictors for each target series.
Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality
Parameter learning for high-dimensional, partially observed, and nonlinear stochastic processes is a methodological challenge.
Convergence of Dirichlet Forms for MCMC Optimal Scaling with Dependent Target Distributions on Large Graphs
Markov chain Monte Carlo (MCMC) algorithms have played a significant role in statistics, physics, machine learning and others, and they are the only known general and efficient approach for some high-dimensional problems.
