4 citations · 6 across the 16 of their papers we have counts for
6 papers · 1 filter
Out-of-Distribution Detection through Soft Clustering with Non-Negative Kernel Regression
Aryan Gulati, Xingjian Dong, Carlos Hurtado +3
As language models become more general purpose, increased attention needs to be paid to detecting out-of-distribution (OOD) instances, i.e., those not belonging to any of the distr…
Study of Manifold Geometry using Multiscale Non-Negative Kernel Graphs
Carlos Hurtado, Sarath Shekkizhar, Javier Ruiz-Hidalgo +1
Modern machine learning systems are increasingly trained on large amounts of data embedded in high-dimensional spaces. Often this is done without analyzing the structure of the dat…
The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning
Romain Cosentino, Sarath Shekkizhar, Mahdi Soltanolkotabi +2
Self-supervised learning (SSL) has emerged as a desirable paradigm in computer vision due to the inability of supervised models to learn representations that can generalize in doma…
Channel redundancy and overlap in convolutional neural networks with channel-wise NNK graphs
David Bonet, Antonio Ortega, Javier Ruiz-Hidalgo +1
Feature spaces in the deep layers of convolutional neural networks (CNNs) are often very high-dimensional and difficult to interpret. However, convolutional layers consist of multi…
NNK-Means: Data summarization using dictionary learning with non-negative kernel regression
Sarath Shekkizhar, Antonio Ortega
An increasing number of systems are being designed by gathering significant amounts of data and then optimizing the system parameters directly using the obtained data. Often this i…
Neighborhood and Graph Constructions using Non-Negative Kernel Regression
Sarath Shekkizhar, Antonio Ortega
Data-driven neighborhood definitions and graph constructions are often used in machine learning and signal processing applications. k-nearest neighbor~(kNN) and -neighborhood me…