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Domain Generalization -- A Causal Perspective
As a result models that rely on this assumption exhibit poor generalization capabilities.
IoT Solutions with Multi-Sensor Fusion and Signal-Image Encoding for Secure Data Transfer and Decision Making
Deployment of Internet of Things (IoT) devices and Data Fusion techniques have gained popularity in public and government domains.
Image-Audio Encoding to Improve C2 Decision-Making in Multi-Domain Environment
The military is investigating methods to improve communication and agility in its multi-domain operations (MDO).
Causal Inference for Time series Analysis: Problems, Methods and Evaluation
In this paper, we focus on two causal inference tasks, i. e., treatment effect estimation and causal discovery for time series data, and provide a comprehensive review of the approaches in each task.
Causal Adversarial Network for Learning Conditional and Interventional Distributions
The LGN is a GAN-based architecture which learns and samples from the causal model over labels.
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation
In this work, models that aim to answer causal questions are referred to as causal interpretable models.
Deep causal representation learning for unsupervised domain adaptation
Recent research on deep domain adaptation proposed to mitigate this problem by forcing the deep model to learn more transferable feature representations across domains.
