Search Results for author:
Found 17 papers, 3 papers with code
Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning
This paper investigates the use of probabilistic neural networks (PNNs) to model aleatoric uncertainty, which refers to the inherent variability in the input-output relationships of a system, often characterized by unequal variance or heteroscedasticity.
Adaptive Activation Functions for Predictive Modeling with Sparse Experimental Data
A pivotal aspect in the design of neural networks lies in selecting activation functions, crucial for introducing nonlinear structures that capture intricate input-output patterns.
Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation
This paper introduces a novel two-stage machine learning-based surrogate modeling framework to address inverse problems in scientific and engineering fields.
D-CBRS: Accounting For Intra-Class Diversity in Continual Learning
Continual learning -- accumulating knowledge from a sequence of learning experiences -- is an important yet challenging problem.
An Empirical Evaluation of the t-SNE Algorithm for Data Visualization in Structural Engineering
A fundamental task in machine learning involves visualizing high-dimensional data sets that arise in high-impact application domains.
Kernel Ridge Regression Using Importance Sampling with Application to Seismic Response Prediction
The first contribution is to propose a novel landmark selection method that promotes diversity using an efficient two-step approach.
Scalable Spectral Clustering with Nystrom Approximation: Practical and Theoretical Aspects
To address the limitations, this work presents a principled spectral clustering algorithm that exploits spectral properties of the similarity matrix associated with sampled points to regulate accuracy-efficiency trade-offs.
Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud
This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds.
The Effectiveness of Variational Autoencoders for Active Learning
The high cost of acquiring labels is one of the main challenges in deploying supervised machine learning algorithms.
Large-Scale Sparse Subspace Clustering Using Landmarks
Subspace clustering methods based on expressing each data point as a linear combination of all other points in a dataset are popular unsupervised learning techniques.
Efficient Solvers for Sparse Subspace Clustering
The $\ell_0$ model is non-convex but only needs memory linear in $n$, and is solved via orthogonal matching pursuit and cannot handle the case of affine subspaces.
Improved Fixed-Rank Nyström Approximation via QR Decomposition: Practical and Theoretical Aspects
The Nystrom method is a popular technique that uses a small number of landmark points to compute a fixed-rank approximation of large kernel matrices that arise in machine learning problems.
Randomized Clustered Nystrom for Large-Scale Kernel Machines
Moreover, we introduce a randomized algorithm for generating landmark points that is scalable to large-scale data sets.
A Randomized Approach to Efficient Kernel Clustering
Kernel-based K-means clustering has gained popularity due to its simplicity and the power of its implicit non-linear representation of the data.
Estimation of the sample covariance matrix from compressive measurements
Experimental results demonstrate that our approach allows for accurate estimation of the sample covariance matrix on several real-world data sets, including video data.
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 a small percentage of the components of each data sample.
Efficient Dictionary Learning via Very Sparse Random Projections
Performing signal processing tasks on compressive measurements of data has received great attention in recent years.
