Meta-learning approaches for learning-to-learn in deep learning: A survey
Y Tian, X Zhao, W Huang - Neurocomputing, 2022 - Elsevier
Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …
representation and understand scattered data properties. It has gained considerable …
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community
Artificial Intelligence of Things (AIoT) is an emerging frontier based on the deep fusion of
Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The fundamental goal of …
Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The fundamental goal of …
Context-aware road travel time estimation by coupled tensor decomposition based on trajectory data
Urban road travel time estimation and prediction on a citywide scale is a necessary and
important task for recommending optimal travel paths. However, this problem has not yet …
important task for recommending optimal travel paths. However, this problem has not yet …
Urban traffic dynamics prediction—a continuous spatial-temporal meta-learning approach
Urban traffic status (eg, traffic speed and volume) is highly dynamic in nature, namely,
varying across space and evolving over time. Thus, predicting such traffic dynamics is of …
varying across space and evolving over time. Thus, predicting such traffic dynamics is of …
MulSTE: A Multi-view Spatio-temporal Learning Framework with Heterogeneous Event Fusion for Demand-supply Prediction
Recently, integrated warehouse and distribution logistics systems are widely used in E-
commerce industries to adjust to constantly changing customer demands. It makes the …
commerce industries to adjust to constantly changing customer demands. It makes the …
A Model Classifying Four Classes of Defects in Reinforced Concrete Bridge Elements Using Convolutional Neural Networks
R Trach - Infrastructures, 2023 - mdpi.com
Recently, the bridge infrastructure in Ukraine has faced the problem of having a significant
number of damaged bridges. It is obvious that the repair and restoration of bridges should …
number of damaged bridges. It is obvious that the repair and restoration of bridges should …
MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction
Network traffic prediction techniques have attracted much attention since they are valuable
for network congestion control and user experience improvement. While existing prediction …
for network congestion control and user experience improvement. While existing prediction …
Storm-gan: spatio-temporal meta-gan for cross-city estimation of human mobility responses to covid-19
Human mobility estimation is crucial during the COVID-19 pandemic due to its significant
guidance for policymakers to make non-pharmaceutical interventions. While deep learning …
guidance for policymakers to make non-pharmaceutical interventions. While deep learning …
Region Profile Enhanced Urban Spatio-Temporal Prediction via Adaptive Meta-Learning
Urban spatio-temporal (ST) prediction plays a crucial role in smart city construction. Due to
the high cost of ST data collection, improving ST prediction in a lack of data is significant. For …
the high cost of ST data collection, improving ST prediction in a lack of data is significant. For …
Recursive logit-based meta-inverse reinforcement learning for driver-preferred route planning
P Zhang, D Lei, S Liu, H Jiang - … research part E: logistics and transportation …, 2024 - Elsevier
Driver-preferred route planning often evaluates the quality of a planned route based on how
closely it is followed by the driver. Despite decades of research in this area, there still exist …
closely it is followed by the driver. Despite decades of research in this area, there still exist …