research
∙
08/26/2023
Out-of-distribution detection using normalizing flows on the data manifold
A common approach for out-of-distribution detection involves estimating ...
share
research
∙
03/06/2023
Combination of Single and Multi-frame Image Super-resolution: An Analytical Perspective
Super-resolution is the process of obtaining a high-resolution image fro...
research
∙
12/11/2022
Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition
Numerous models have tried to effectively embed knowledge graphs in low ...
research
∙
12/11/2022
Stochastic First-Order Learning for Large-Scale Flexibly Tied Gaussian Mixture Model
Gaussian Mixture Models (GMM) are one of the most potent parametric dens...
research
∙
08/07/2022
Fast Online and Relational Tracking
To overcome challenges in multiple object tracking task, recent algorith...
research
∙
06/07/2022
Joint Manifold Learning and Density Estimation Using Normalizing Flows
Based on the manifold hypothesis, real-world data often lie on a low-dim...
research
∙
06/02/2022
Introducing One Sided Margin Loss for Solving Classification Problems in Deep Networks
This paper introduces a new loss function, OSM (One-Sided Margin), to so...
research
∙
12/20/2021
Self-attention Presents Low-dimensional Knowledge Graph Embeddings for Link Prediction
Recently, link prediction problem, also known as knowledge graph complet...
research
∙
11/01/2021
PerSpeechNorm: A Persian Toolkit for Speech Processing Normalization
In general, speech processing models consist of a language model along w...
research
∙
08/29/2021
Solving Viewing Graph Optimization for Simultaneous Position and Rotation Registration
A viewing graph is a set of unknown camera poses, as the vertices, and t...
research
∙
07/09/2021
Optimal Triangulation Method is Not Really Optimal
Triangulation refers to the problem of finding a 3D point from its 2D pr...
research
∙
04/21/2021
Accurate and fast matrix factorization for low-rank learning
In this paper we tackle two important challenges related to the accurate...
research
∙
03/11/2021
Learning with partially separable data
There are partially separable data types that make classification tasks ...
research
∙
03/06/2021
Simple online and real-time tracking with occlusion handling
Multiple object tracking is a challenging problem in computer vision due...
research
∙
12/13/2020
Reinforcement Learning with Subspaces using Free Energy Paradigm
In large-scale problems, standard reinforcement learning algorithms suff...
research
∙
10/27/2020
Contour Integration using Graph-Cut and Non-Classical Receptive Field
Many edge and contour detection algorithms give a soft-value as an outpu...
research
∙
08/05/2020
FRMDN: Flow-based Recurrent Mixture Density Network
Recurrent Mixture Density Networks (RMDNs) are consisted of two main par...
research
∙
12/07/2018
Deep-RBF Networks Revisited: Robust Classification with Rejection
One of the main drawbacks of deep neural networks, like many other class...
research
∙
06/01/2018
A Recursive Least Square Method for 3D Pose Graph Optimization Problem
Pose Graph Optimization (PGO) is an important non-convex optimization pr...
research
∙
10/22/2017
Exploiting generalization in the subspaces for faster model-based learning
Due to the lack of enough generalization in the state-space, common meth...
research
∙
06/10/2017
An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimization
We consider maximum likelihood estimation for Gaussian Mixture Models (G...
research
∙
07/18/2016
Geometric Mean Metric Learning
We revisit the task of learning a Euclidean metric from data. We approac...
research
∙
06/25/2015
Manifold Optimization for Gaussian Mixture Models
We take a new look at parameter estimation for Gaussian Mixture Models (...
research
∙
10/17/2014
Inference and Mixture Modeling with the Elliptical Gamma Distribution
We study modeling and inference with the Elliptical Gamma Distribution (...
research
∙
09/20/2011