When Gaussian process meets big data: A review of scalable GPs
The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …
hardware encourages success stories in the machine learning community. In the …
Priors in bayesian deep learning: A review
V Fortuin - International Statistical Review, 2022 - Wiley Online Library
While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …
recent Bayesian deep learning models have often fallen back on vague priors, such as …
Causal inference for time series analysis: Problems, methods and evaluation
Time series data are a collection of chronological observations which are generated by
several domains such as medical and financial fields. Over the years, different tasks such as …
several domains such as medical and financial fields. Over the years, different tasks such as …
[PDF][PDF] 时间序列预测方法综述
杨海民， 潘志松， 白玮 - 计算机科学, 2019 - qn-next.xuetangx.com
摘要时间序列是按照时间排序的一组随机变量, 它通常是在相等间隔的时间段内依照给定的采样
率对某种潜在过程进行观测的结果. 时间序列数据本质上反映的是某个或者某些随机变量随时间 …
率对某种潜在过程进行观测的结果. 时间序列数据本质上反映的是某个或者某些随机变量随时间 …
Autoregressive convolutional neural networks for asynchronous time series
Abstract We propose Significance-Offset Convolutional Neural Network, a deep
convolutional network architecture for regression of multivariate asynchronous time series …
convolutional network architecture for regression of multivariate asynchronous time series …
A unifying framework for Gaussian process pseudo-point approximations using power expectation propagation
Gaussian processes (GPs) are flexible distributions over functions that enable highlevel
assumptions about unknown functions to be encoded in a parsimonious, flexible and …
assumptions about unknown functions to be encoded in a parsimonious, flexible and …
Multiple time series forecasting with dynamic graph modeling
Multiple time series forecasting plays an essential role in many applications. Solutions
based on graph neural network (GNN) that deliver state-of-the-art forecasting performance …
based on graph neural network (GNN) that deliver state-of-the-art forecasting performance …
A review of Shannon and differential entropy rate estimation
A Feutrill, M Roughan - Entropy, 2021 - mdpi.com
In this paper, we present a review of Shannon and differential entropy rate estimation
techniques. Entropy rate, which measures the average information gain from a stochastic …
techniques. Entropy rate, which measures the average information gain from a stochastic …
Mixture optimization of recycled aggregate concrete using hybrid machine learning model
Recycled aggregate concrete (RAC) contributes to mitigating the depletion of natural
aggregates, alleviating the carbon footprint of concrete construction, and averting the …
aggregates, alleviating the carbon footprint of concrete construction, and averting the …
Spectral mixture kernels for multi-output Gaussian processes
G Parra, F Tobar - Advances in Neural Information …, 2017 - proceedings.neurips.cc
Early approaches to multiple-output Gaussian processes (MOGPs) relied on linear
combinations of independent, latent, single-output Gaussian processes (GPs). This resulted …
combinations of independent, latent, single-output Gaussian processes (GPs). This resulted …