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Edge-cloud polarization and collaboration: A comprehensive survey for ai
Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …
Causal discovery from temporal data: An overview and new perspectives
Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …
been a typical data structure that can be widely generated by many domains, such as …
[HTML][HTML] GARNN: an interpretable graph attentive recurrent neural network for predicting blood glucose levels via multivariate time series
Accurate prediction of future blood glucose (BG) levels can effectively improve BG
management for people living with type 1 or 2 diabetes, thereby reducing complications and …
management for people living with type 1 or 2 diabetes, thereby reducing complications and …
Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data
Granger causality, commonly used for inferring causal structures from time series data, has
been adopted in widespread applications across various fields due to its intuitive …
been adopted in widespread applications across various fields due to its intuitive …
Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems
Understanding causal relationships within Gene Regulatory Networks (GRNs) is essential
for unraveling the gene interactions in cellular processes. However, causal discovery in …
for unraveling the gene interactions in cellular processes. However, causal discovery in …
KDBI special issue: Explainability feature selection framework application for LSTM multivariate time‐series forecast self optimization
Deep learning models are widely used in multivariate time series forecasting, yet, they have
high computational costs. One way to reduce this cost is by reducing data dimensionality …
high computational costs. One way to reduce this cost is by reducing data dimensionality …
CausalMMM: Learning Causal Structure for Marketing Mix Modeling
In online advertising, marketing mix modeling (MMM) is employed to predict the gross
merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget …
merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget …
A strategy of model space search for dynamic causal modeling in task fMRI data exploratory analysis
Dynamic causal modeling (DCM) is a tool used for effective connectivity (EC) estimation in
neuroimage analysis. But it is a model-driven analysis method, and the structure of the EC …
neuroimage analysis. But it is a model-driven analysis method, and the structure of the EC …
Causal Discovery in Time Series Data Using Deep Learning Techniques
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Causal structure learning from observational data has been an active field of research over
the past decades. In the literature, different algorithms and models have been proposed …
the past decades. In the literature, different algorithms and models have been proposed …