research
∙
07/13/2022
D-CBRS: Accounting For Intra-Class Diversity in Continual Learning
Continual learning – accumulating knowledge from a sequence of learning ...
share
research
∙
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...
research
∙
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...
research
∙
08/03/2020
Uncertainty Quantification of Structural Systems with Subset of Data
Quantification of the impact of uncertainty in material properties as we...
research
∙
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) ...
research
∙
06/25/2020
Scalable Spectral Clustering with Nystrom Approximation: Practical and Theoretical Aspects
Spectral clustering techniques are valuable tools in signal processing a...
research
∙
06/07/2020
Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud
This article proposes a novel unsupervised learning framework for detect...
research
∙
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...
research
∙
08/02/2019
Large-Scale Sparse Subspace Clustering Using Landmarks
Subspace clustering methods based on expressing each data point as a lin...
research
∙
04/17/2018
Efficient Solvers for Sparse Subspace Clustering
Sparse subspace clustering (SSC) is a popular method in machine learning...
research
∙
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...
research
∙
12/20/2016
Randomized Clustered Nystrom for Large-Scale Kernel Machines
The Nystrom method has been popular for generating the low-rank approxim...
research
∙
08/26/2016
A Randomized Approach to Efficient Kernel Clustering
Kernel-based K-means clustering has gained popularity due to its simplic...
research
∙
12/30/2015
Estimation of the sample covariance matrix from compressive measurements
This paper focuses on the estimation of the sample covariance matrix fro...
research
∙
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 ...
research
∙
04/05/2015