A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions
Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …
groups so that similar samples belong to the same cluster while dissimilar samples belong …
Data based identification and prediction of nonlinear and complex dynamical systems
The problem of reconstructing nonlinear and complex dynamical systems from measured
data or time series is central to many scientific disciplines including physical, biological …
data or time series is central to many scientific disciplines including physical, biological …
Mutual information neural estimation
We argue that the estimation of mutual information between high dimensional continuous
random variables can be achieved by gradient descent over neural networks. We present a …
random variables can be achieved by gradient descent over neural networks. We present a …
Oil well production prediction based on CNN-LSTM model with self-attention mechanism
S Pan, B Yang, S Wang, Z Guo, L Wang, J Liu, S Wu - Energy, 2023 - Elsevier
To overcome the shortcomings in current study of oil well production prediction, we propose
a combined model (CNN-LSTM-SA) with the convolutional neural network (CNN), the long …
a combined model (CNN-LSTM-SA) with the convolutional neural network (CNN), the long …
Lineage tracing on transcriptional landscapes links state to fate during differentiation
INTRODUCTION During tissue turnover, stem and progenitor cells differentiate to produce
mature cell types. To understand and ultimately control differentiation, it is important to …
mature cell types. To understand and ultimately control differentiation, it is important to …
Mine: mutual information neural estimation
We argue that the estimation of mutual information between high dimensional continuous
random variables can be achieved by gradient descent over neural networks. We present a …
random variables can be achieved by gradient descent over neural networks. We present a …
Causalvae: Disentangled representation learning via neural structural causal models
Learning disentanglement aims at finding a low dimensional representation which consists
of multiple explanatory and generative factors of the observational data. The framework of …
of multiple explanatory and generative factors of the observational data. The framework of …
Urban water demand prediction for a city that suffers from climate change and population growth: Gauteng province case study
The proper management of a municipal water system is essential to sustain cities and
support the water security of societies. Urban water estimating has always been a …
support the water security of societies. Urban water estimating has always been a …
A variable selection method based on mutual information and variance inflation factor
J Cheng, J Sun, K Yao, M Xu, Y Cao - Spectrochimica Acta Part A …, 2022 - Elsevier
Feature selection plays a vital role in the quantitative analysis of high-dimensional data to
reduce dimensionality. Recently, the variable selection method based on mutual information …
reduce dimensionality. Recently, the variable selection method based on mutual information …
Gene regulatory network inference from single-cell data using multivariate information measures
While single-cell gene expression experiments present new challenges for data processing,
the cell-to-cell variability observed also reveals statistical relationships that can be used by …
the cell-to-cell variability observed also reveals statistical relationships that can be used by …