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Found 6 papers, 0 papers with code
FORML: A Riemannian Hessian-free Method for Meta-learning with Orthogonality Constraint
However, performing the optimization in the Riemannian space, where the parameters and meta-parameters are located on Riemannian manifolds is computationally intensive.
Multi-modal reward for visual relationships-based image captioning
To take advantage of visual relationships in caption generation, this paper proposes a deep neural network architecture for image captioning based on fusing the visual relationships information extracted from an image's scene graph with the spatial feature maps of the image.
Geometric Multimodal Deep Learning with Multi-Scaled Graph Wavelet Convolutional Network
Capturing the intra-modality and cross-modality information of multimodal data is the essential capability of multimodal learning methods.
Cross-Modal and Multimodal Data Analysis Based on Functional Mapping of Spectral Descriptors and Manifold Regularization
The second method is a manifold regularized multimodal classification based on pointwise correspondences (M$^2$CPC) used for the problem of multiclass classification of multimodal heterogeneous, which the correspondences between modalities are determined based on the FMBSD method.
Weighted Least Squares Twin Support Vector Machine with Fuzzy Rough Set Theory for Imbalanced Data Classification
In this work, we propose an approach that efficiently used fuzzy rough set theory in weighted least squares twin support vector machine called FRLSTSVM for classification of imbalanced data.
Machine learning for Internet of Things data analysis: A survey
This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case.
