This study investigates the effect of manifold learning using normalizing flows on out-of-distribution detection.
Super-resolution is the process of obtaining a high-resolution image from one or more low-resolution images.
Many Graph Neural Networks (GNNs) are proposed for Knowledge Graph Embedding (KGE).
Gaussian Mixture Models (GMMs) are one of the most potent parametric density models used extensively in many applications.
In this paper, a method for measuring camera motion and removing its effect is presented that efficiently reduces the camera motion effect on tracking.
We propose a single-step method for joint manifold learning and density estimation by disentangling the transformed space obtained by normalizing flows to manifold and off-manifold parts.
OSM has consistently shown better classification accuracies over cross-entropy and hinge losses for small to large neural networks.
Notably, we yield our promising results with a significant reduction of 66. 9% in the dimensionality of embeddings compared to the five best recent state-of-the-art competitors on average.
Ranked #16 on Link Prediction on FB15k-237
Comparison with other available Persian textual normalization tools indicates the superiority of the proposed method in speech processing.
A viewing graph is a set of unknown camera poses, as the vertices, and the observed relative motions, as the edges.
Riemannian LBFGS (RLBFGS) is an extension of this method to Riemannian manifolds.
Therefore, in contrast to the common practice, we argue that the simple mid-point method should be used in structure-from-motion applications where there is uncertainty in camera parameters.
In this paper, we tackle two important problems in low-rank learning, which are partial singular value decomposition and numerical rank estimation of huge matrices.
In this study, we propose a framework for the classification of partially separable data types that are not classifiable using typical methods.
In contrast, there are algorithms that only use motion cues to increase speed, especially for online applications.
We propose a free-energy minimization framework for selecting the subspaces and integrate the policy of the state-space into the subspaces.
Many edge and contour detection algorithms give a soft-value as an output and the final binary map is commonly obtained by applying an optimal threshold.
The class of recurrent mixture density networks is an important class of probabilistic models used extensively in sequence modeling and sequence-to-sequence mapping applications.
We benefit from transfer learning using a pre-trained CNN for feature learning.
On the other hand, deep-RBF networks assign high confidence only to the regions containing enough feature points, but they have been discounted due to the widely-held belief that they have the vanishing gradient problem.
Generalization and faster learning in a subspace are due to many-to-one mapping of experiences from the full-space to each state in the subspace.
This motivates us to take a closer look at the problem geometry, and derive a better formulation that is much more amenable to Riemannian optimization.
Mixture models are powerful statistical models used in many applications ranging from density estimation to clustering and classification.
We exploit the remarkable structure of the convex cone of positive definite matrices which allows one to uncover hidden geodesic convexity of objective functions that are nonconvex in the ordinary Euclidean sense.