Results 41 to 50 of about 375,534 (168)
Efficient Smooth Tensor Train and Tensor Ring Completion for Image Classification Enhancement
IEEE AccessThis paper deals with studying the data completion problem for enhancing the image classification task under the pixel removal scenario. In some applications, it happens that a part of the pixels of a given image is lost due to several issues, such as ...
Salman Ahmadi-Asl +5 more
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An Optimized Network Representation Learning Algorithm Using Multi-Relational Data
Mathematics, 2019 Representation learning aims to encode the relationships of research objects into low-dimensional, compressible, and distributed representation vectors.
Zhonglin Ye +4 more
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Lattice Representation Learning
CoRR, 2020 In this article we introduce theory and algorithms for learning discrete representations that take on a lattice that is embedded in an Euclidean space. Lattice representations possess an interesting combination of properties: a) they can be computed explicitly using lattice quantization, yet they can be learned efficiently using the ideas we introduce ...
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Frontiers in MedicineThe emergence of both task-specific single-modality models and general-purpose multimodal large models presents new opportunities, but also introduces challenges, particularly regarding adversarial attacks.
Ekaterina Mozhegova +5 more
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Learning Tensor Representations for Meta-Learning
CoRR, 2022 We introduce a tensor-based model of shared representation for meta-learning from a diverse set of tasks. Prior works on learning linear representations for meta-learning assume that there is a common shared representation across different tasks, and do not consider the additional task-specific observable side information.
Samuel Deng +3 more
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Ultrahyperbolic Representation Learning
CoRR, 2020 In machine learning, data is usually represented in a (flat) Euclidean space where distances between points are along straight lines. Researchers have recently considered more exotic (non-Euclidean) Riemannian manifolds such as hyperbolic space which is well suited for tree-like data.
Marc T. Law, Jos Stam
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Meta-learning of Textual Representations [PDF]
, 2020 Recent progress in AutoML has lead to state-of-the-art methods (e.g., AutoSKLearn) that can be readily used by non-experts to approach any supervised learning problem. Whereas these methods are quite effective, they are still limited in the sense that they work for tabular (matrix formatted) data only. This paper describes one step forward in trying to
Jorge G. Madrid +2 more
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Knowledge Graphs in Education and Employability: A Survey on Applications and Techniques
IEEE Access, 2022 Studies on the relationship between education and employability are of paramount importance for policy makers, training institutions, companies and students. The availability of large scale data on the Internet, such as online job ads, has been leveraged
Yousra Fettach +2 more
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Entity Profiling in Knowledge Graphs
IEEE Access, 2020 Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual information about real-world entities. Understanding the uniqueness of each entity is crucial to the analyzing, sharing, and reusing of KGs.
Xiang Zhang +3 more
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Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification
Diagnostics, 2022 High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data.
Aiham Taleb +7 more
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