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Found 31 papers, 13 papers with code
Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detections
Anomaly Detection (AD) involves identifying deviations from normal data distributions and is critical in fields such as medical diagnostics and industrial defect detection.
Subgoal Discovery Using a Free Energy Paradigm and State Aggregations
Reinforcement learning (RL) plays a major role in solving complex sequential decision-making tasks.
Artificial Data Point Generation in Clustered Latent Space for Small Medical Datasets
One of the growing trends in machine learning is the use of data generation techniques, since the performance of machine learning models is dependent on the quantity of the training dataset.
Out-of-distribution detection using normalizing flows on the data manifold
Therefore, we proceed by estimating the density on a low-dimensional manifold and calculating a distance from the manifold as a measure for out-of-distribution detection.
Combination of Single and Multi-frame Image Super-resolution: An Analytical Perspective
Super-resolution is the process of obtaining a high-resolution image from one or more low-resolution images.
Stochastic First-Order Learning for Large-Scale Flexibly Tied Gaussian Mixture Model
Gaussian Mixture Models (GMMs) are one of the most potent parametric density models used extensively in many applications.
Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition
Numerous Graph Neural Networks (GNNs) have been developed to tackle the challenge of Knowledge Graph Embedding (KGE).
Fast Online and Relational Tracking
In this paper, a method for measuring camera motion and removing its effect is presented that efficiently reduces the camera motion effect on tracking.
Joint Manifold Learning and Density Estimation Using Normalizing Flows
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.
Introducing One Sided Margin Loss for Solving Classification Problems in Deep Networks
OSM has consistently shown better classification accuracies over cross-entropy and hinge losses for small to large neural networks.
Self-attention Presents Low-dimensional Knowledge Graph Embeddings for Link Prediction
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 #17 on
Link Prediction
on FB15k-237
ParsiNorm: A Persian Toolkit for Speech Processing Normalization
Comparison with other available Persian textual normalization tools indicates the superiority of the proposed method in speech processing.
Solving Viewing Graph Optimization for Simultaneous Position and Rotation Registration
A viewing graph is a set of unknown camera poses, as the vertices, and the observed relative motions, as the edges.
Vector Transport Free Riemannian LBFGS for Optimization on Symmetric Positive Definite Matrix Manifolds
Riemannian LBFGS (RLBFGS) is an extension of this method to Riemannian manifolds.
Optimal Triangulation Method is Not Really Optimal
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.
Accurate and fast matrix factorization for low-rank learning
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.
Matrix Factorization / Decomposition
Riemannian optimization
Learning with partially separable data
In this study, we propose a framework for the classification of partially separable data types that are not classifiable using typical methods.
Simple online and real-time tracking with occlusion handling
In contrast, there are algorithms that only use motion cues to increase speed, especially for online applications.
Reinforcement Learning with Subspaces using Free Energy Paradigm
We propose a free-energy minimization framework for selecting the subspaces and integrate the policy of the state-space into the subspaces.
Contour Integration using Graph-Cut and Non-Classical Receptive Field
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.
FRMDN: Flow-based Recurrent Mixture Density Network
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.
Active Transfer Learning for Persian Offline Signature Verification
We benefit from transfer learning using a pre-trained CNN for feature learning.
Deep-RBF Networks Revisited: Robust Classification with Rejection
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.
Exploiting generalization in the subspaces for faster model-based learning
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.
An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimization
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.
Matrix Manifold Optimization for Gaussian Mixtures
We take a new look at parameter estimation for Gaussian Mixture Model (GMMs).
MixEst: An Estimation Toolbox for Mixture Models
Mixture models are powerful statistical models used in many applications ranging from density estimation to clustering and classification.
Manifold Optimization for Gaussian Mixture Models
We take a new look at parameter estimation for Gaussian Mixture Models (GMMs).
Inference and Mixture Modeling with the Elliptical Gamma Distribution
We study modeling and inference with the Elliptical Gamma Distribution (EGD).
Geometric optimisation on positive definite matrices for elliptically contoured distributions
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.
