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Deep Multimodal Representation Learning from Temporal Data
In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint representations in data fusion applications.
Bernal, Edgar A. +5 more
core +1 more source
Tile2Vec: Unsupervised representation learning for spatially distributed data
Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks.
Azzari, George +5 more
core +1 more source
Efficient Data Representation by Selecting Prototypes with Importance Weights
Prototypical examples that best summarizes and compactly represents an underlying complex data distribution communicate meaningful insights to humans in domains where simple explanations are hard to extract.
Aggarwal, Charu +3 more
core +1 more source
Representation Independent Analytics Over Structured Data [PDF]
Database analytics algorithms leverage quantifiable structural properties of the data to predict interesting concepts and relationships. The same information, however, can be represented using many different structures and the structural properties ...
Chodpathumwan, Yodsawalai +4 more
core
Object detection is an essential computer vision task that identifies and locates objects within images or videos and is crucial for applications such as autonomous driving, robotics, and augmented reality.
Yujing Wang +3 more
doaj +1 more source
Data and information quality have been recognized as essential components for improving business efficiency. One approach for the assessment of information quality (IQ) is the manufacturing of information (MI).
Monica Blasco-Lopez +4 more
doaj +1 more source
Improving generative inverse design of molecular catalysts in small data regime
Deep generative models are a powerful tool for exploring the chemical space within inverse-design workflows; however, their effectiveness relies on sufficient training data and effective mechanisms for guiding the model to optimize specific properties ...
François Cornet +4 more
doaj +1 more source
Robust Local Learning and Discriminative Concept Factorization for Data Representation
Concept factorization (CF), as a matrix factorization method, has been applied widely in obtaining an optimal data representation and has yielded impressive results. However, some shortcomings exist in the existing CF method.
Wei Jiang +4 more
doaj +1 more source
Many marine monitoring infrastructures continuously collect biological and abiotic data, yet user-friendly interfaces for visualizing and translating this knowledge remain limited.
Morane Clavel-Henry +27 more
doaj +1 more source
Most open repositories present a similar interface and workflow to publish data resultant from different types of research methods. Publishing simulation datasets is challenging due to the iterative nature of simulations that generate large numbers and ...
Maria Esteva +5 more
doaj +1 more source

