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AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System
This refinement is crucial in effectively identifying genuine threats to pipelines, thus enhancing the safety of energy transportation.
Metadata-Based RAW Reconstruction via Implicit Neural Functions
Many low-level computer vision tasks are desirable to utilize the unprocessed RAW image as input, which remains the linear relationship between pixel values and scene radiance.
DBT-DMAE: An Effective Multivariate Time Series Pre-Train Model under Missing Data
Multivariate time series(MTS) is a universal data type related to many practical applications.
Rethinking Controllable Variational Autoencoders
It has shown success in a variety of applications, such as image generation, disentangled representation learning, and language modeling.
DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning
Existing methods, such as $\beta$-VAE and FactorVAE, assign a large weight to the KL-divergence term in the objective function, leading to high reconstruction errors for the sake of better disentanglement.
Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation
Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximation to handle large state spaces in practice.
