156 citations · 522 across the 37 of their papers we have counts for
5 papers · 1 filter
Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition
Peyman Baghershahi, Reshad Hosseini, Hadi Moradi
Numerous Graph Neural Networks (GNNs) have been developed to tackle the challenge of Knowledge Graph Embedding (KGE). However, many of these approaches overlook the crucial role of…
Stochastic First-Order Learning for Large-Scale Flexibly Tied Gaussian Mixture Model
Mohammad Pasande, Reshad Hosseini, Babak Nadjar Araabi
Gaussian Mixture Models (GMMs) are one of the most potent parametric density models used extensively in many applications. Flexibly-tied factorization of the covariance matrices in…
Fast Online and Relational Tracking
Mohammad Hossein Nasseri, Mohammadreza Babaee, Hadi Moradi +1
To overcome challenges in multiple object tracking task, recent algorithms use interaction cues alongside motion and appearance features. These algorithms use graph neural networks…
Joint Manifold Learning and Density Estimation Using Normalizing Flows
Seyedeh Fatemeh Razavi, Mohammad Mahdi Mehmanchi, Reshad Hosseini +1
Based on the manifold hypothesis, real-world data often lie on a low-dimensional manifold, while normalizing flows as a likelihood-based generative model are incapable of finding t…
Introducing One Sided Margin Loss for Solving Classification Problems in Deep Networks
Ali Karimi, Zahra Mousavi Kouzehkanan, Reshad Hosseini +1
This paper introduces a new loss function, OSM (One-Sided Margin), to solve maximum-margin classification problems effectively. Unlike the hinge loss, in OSM the margin is explicit…