Farhad Pourkamali-Anaraki | DeepAI

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Ali-akbar Agha-mohammadi
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Christoforos Kanellakis
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∙ 07/13/2022

D-CBRS: Accounting For Intra-Class Diversity in Continual Learning

Continual learning – accumulating knowledge from a sequence of learning ...

0 Yasin Findik, et al. ∙

∙ 09/18/2021

An Empirical Evaluation of the t-SNE Algorithm for Data Visualization in Structural Engineering

A fundamental task in machine learning involves visualizing high-dimensi...

0 Parisa Hajibabaee, et al. ∙

∙ 09/19/2020

Kernel Ridge Regression Using Importance Sampling with Application to Seismic Response Prediction

Scalable kernel methods, including kernel ridge regression, often rely o...

0 Farhad Pourkamali-Anaraki, et al. ∙

∙ 08/03/2020

Uncertainty Quantification of Structural Systems with Subset of Data

Quantification of the impact of uncertainty in material properties as we...

0 Mohammad Amin Hariri-Ardebili, et al. ∙

∙ 07/31/2020

A Unified NMPC Scheme for MAVs Navigation with 3D Collision Avoidance under Position Uncertainty

This article proposes a novel Nonlinear Model Predictive Control (NMPC) ...

0 Sina Sharif Mansouri, et al. ∙

∙ 06/25/2020

Scalable Spectral Clustering with Nystrom Approximation: Practical and Theoretical Aspects

Spectral clustering techniques are valuable tools in signal processing a...

0 Farhad Pourkamali-Anaraki, et al. ∙

∙ 06/07/2020

Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud

This article proposes a novel unsupervised learning framework for detect...

0 Sina Sharif Mansouri, et al. ∙

∙ 11/18/2019

The Effectiveness of Variational Autoencoders for Active Learning

The high cost of acquiring labels is one of the main challenges in deplo...

0 Farhad Pourkamali-Anaraki, et al. ∙

∙ 08/02/2019

Large-Scale Sparse Subspace Clustering Using Landmarks

Subspace clustering methods based on expressing each data point as a lin...

0 Farhad Pourkamali-Anaraki, et al. ∙

∙ 04/17/2018

Efficient Solvers for Sparse Subspace Clustering

Sparse subspace clustering (SSC) is a popular method in machine learning...

0 Farhad Pourkamali-Anaraki, et al. ∙

∙ 08/08/2017

Improved Fixed-Rank Nyström Approximation via QR Decomposition: Practical and Theoretical Aspects

The Nyström method is a popular technique for computing fixed-rank appro...

0 Farhad Pourkamali-Anaraki, et al. ∙

∙ 12/20/2016

Randomized Clustered Nystrom for Large-Scale Kernel Machines

The Nystrom method has been popular for generating the low-rank approxim...

0 Farhad Pourkamali-Anaraki, et al. ∙

∙ 08/26/2016

A Randomized Approach to Efficient Kernel Clustering

Kernel-based K-means clustering has gained popularity due to its simplic...

0 Farhad Pourkamali-Anaraki, et al. ∙

∙ 12/30/2015

Estimation of the sample covariance matrix from compressive measurements

This paper focuses on the estimation of the sample covariance matrix fro...

0 Farhad Pourkamali-Anaraki, et al. ∙

∙ 10/31/2015

Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means

We analyze a compression scheme for large data sets that randomly keeps ...

0 Farhad Pourkamali-Anaraki, et al. ∙

∙ 04/05/2015

Efficient Dictionary Learning via Very Sparse Random Projections

Performing signal processing tasks on compressive measurements of data h...

0 Farhad Pourkamali-Anaraki, et al. ∙

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