Search Results for author: Reza Monsefi

Found 9 papers, 4 papers with code

Hamiltonian Adaptive Importance Sampling

no code implementations27 Sep 2022 Ali Mousavi, Reza Monsefi, Víctor Elvira

Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for approximating integrals, for instance in the context of Bayesian inference.

Bayesian Inference

Deep Learning Applications for Lung Cancer Diagnosis: A systematic review

no code implementations1 Jan 2022 Hesamoddin Hosseini, Reza Monsefi, Shabnam Shadroo

This research is superior to other review articles in this field due to the complete review of relevant articles and systematic write up.

Lung Cancer Diagnosis

A Framework to Enhance Generalization of Deep Metric Learning methods using General Discriminative Feature Learning and Class Adversarial Neural Networks

1 code implementation11 Jun 2021 Karrar Al-Kaabi, Reza Monsefi, Davood Zabihzadeh

To address this limitation, we propose a framework to enhance the generalization power of existing DML methods in a Zero-Shot Learning (ZSL) setting by general yet discriminative representation learning and employing a class adversarial neural network.

Image Retrieval Metric Learning +3

Accurate and fast matrix factorization for low-rank learning

1 code implementation21 Apr 2021 Reza Godaz, Reza Monsefi, Faezeh Toutounian, Reshad Hosseini

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

Neural Generalization of Multiple Kernel Learning

no code implementations26 Feb 2021 Ahmad Navid Ghanizadeh, Kamaledin Ghiasi-Shirazi, Reza Monsefi, Mohammadreza Qaraei

By this interpretation, we propose a Neural Generalization of Multiple Kernel Learning (NGMKL), which extends the conventional multiple kernel learning framework to a multi-layer neural network with nonlinear activation functions.

RELF: Robust Regression Extended with Ensemble Loss Function

no code implementations25 Oct 2018 Hamideh Hajiabadi, Reza Monsefi, Hadi Sadoghi Yazdi

Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one.

Meta-Learning regression

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