37 citations · 78 across the 7 of their papers we have counts for
7 papers
Low-Rank Compression of Pretrained Models via Randomized Subspace Iteration
Farhad Pourkamali-Anaraki
The massive scale of pretrained models has made efficient compression essential for practical deployment. Low-rank decomposition based on the singular value decomposition (SVD) pro…
An Empirical Evaluation of the t-SNE Algorithm for Data Visualization in Structural Engineering
Parisa Hajibabaee, Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili
A fundamental task in machine learning involves visualizing high-dimensional data sets that arise in high-impact application domains. When considering the context of large imbalanc…
Kernel Ridge Regression Using Importance Sampling with Application to Seismic Response Prediction
Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili, Lydia Morawiec
Scalable kernel methods, including kernel ridge regression, often rely on low-rank matrix approximations using the Nystrom method, which involves selecting landmark points from lar…
Uncertainty Quantification of Structural Systems with Subset of Data
Mohammad Amin Hariri-Ardebili, Farhad Pourkamali-Anaraki, Siamak Sattar
Quantification of the impact of uncertainty in material properties as well as the input ground motion on structural responses is an important step in implementing a performance-bas…
A Unified NMPC Scheme for MAVs Navigation with 3D Collision Avoidance under Position Uncertainty
Sina Sharif Mansouri, Christoforos Kanellakis, Bjorn Lindqvist +4
This article proposes a novel Nonlinear Model Predictive Control (NMPC) framework for Micro Aerial Vehicle (MAV) autonomous navigation in constrained environments. The introduced f…
Scalable Spectral Clustering with Nystrom Approximation: Practical and Theoretical Aspects
Farhad Pourkamali-Anaraki
Spectral clustering techniques are valuable tools in signal processing and machine learning for partitioning complex data sets. The effectiveness of spectral clustering stems from…
Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means
Farhad Pourkamali-Anaraki, Stephen Becker
We analyze a compression scheme for large data sets that randomly keeps a small percentage of the components of each data sample. The benefit is that the output is a sparse matrix…