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Found 8 papers, 0 papers with code
An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments
Through our attention mechanism, our method generates a single feature representation of environment given its multiple views.
Attention-based Deep Reinforcement Learning for Multi-view Environments
In reinforcement learning algorithms, it is a common practice to account for only a single view of the environment to make the desired decisions; however, utilizing multiple views of the environment can help to promote the learning of complicated policies.
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining
In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand.
Long-term face tracking in the wild using deep learning
This paper investigates long-term face tracking of a specific person given his/her face image in a single frame as a query in a video stream.
A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip
In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management.
An Adaptive Cluster-based Filtering Framework for Speckle Reduction of OCT Skin Images
The proposed cluster-based filtering framework enables researchers to develop unsupervised learning solutions for de-speckling OCT skin images using adaptive filtering methods, or extend the framework to new applications.
Large-scale Datasets: Faces with Partial Occlusions and Pose Variations in the Wild
The LSLF dataset is currently the largest labeled face image dataset in the literature in terms of the number of labeled images and the number of individuals compared to other existing labeled face image datasets.
Learning Deep Networks from Noisy Labels with Dropout Regularization
Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance.
