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25 papers · ranked by Valyu relevance

01
AarXiv12 Dec 2025
Maritime object classification with SAR imagery using quantum kernel methods
Tanner, John, Davies, Nicholas + 12 more
1 Centre for Quantum Information, Simulation and Algorithms, The University of Western Australia, 35 Stirling Hwy, Crawley WA, 6009, Australia 2 Pawsey Supercomputing Centre, 1 Bryce Avenue, Kensington WA, 6151, Australia 3 School of Physics, Mathematics and Computing, The University of Western Australia, 35 Stirling…
91relevance
02
AarXiv29 Jan 2026
Adaptive Kernel Methods
Tamás Dózsa, Andrea Angino, Zoltán Szabó, József Bokor + 1 more
—Kernel methods approximate nonlinear maps in a data-driven manner by projecting the target map onto a finitedimensional Hilbert space called the solution space. Traditionally, this space is a subspace of a fixed ambient reproducing kernel Hilbert space (RKHS), determined solely by the chosen kernel and the dataset…
86relevance
03
PPubMed27 Jan 2026
Learning cardiac activation and repolarization times with operator learning Learning cardiac activation and repolarization times with operator learning
Giovanni Ziarelli, Edoardo Centofanti, Nicola Parolini, Simone Scacchi + 3 more
Solving partial or ordinary differential equation models in cardiac electrophysiology is a computationally demanding task, particularly when high-resolution meshes are required to capture the complex dynamics of the heart. Moreover, in clinical applications, it is essential to employ computational tools that provide…
82relevance
04
AarXiv29 May 2026
Support Vector Machine with a Scalable Quantum Kernel
Anant Agnihotri, Michael Krebsbach, Florentin Reiter, Thomas Wellens
Quantum support vector machines are classification algorithms that rely on quantum-generated kernels. The fidelity quantum kernel commonly used in quantum support vector machines suffers from exponential concentration as system size increases, preventing an efficient scaling beyond fewqubit systems. We introduce the…
80relevance
05
PPubMed16 Jan 2026
Exploring novel semi-inner product reproducing Kernels in Banach space for robust Kernel methods Semi-inner product reproducing Kernels in Banach space for robust Kernel methods
Yi Ding, Ying Zhao, Yan Pei, Antonio Falcó
Kernel methods are widely applied across various domains; however, structural limitations of reproducing kernels in Hilbert spaces pose significant challenges. Many challenges inherent to Hilbert spaces can be effectively addressed within the framework of Banach spaces. In this work, we define the semi-inner product…
79relevance
06
AarXiv15 Jun 2026
Scalable Pairwise Kernel Learning with Stochastic Vec Trick
Napsu Karmitsa, Tapio Pahikkala, Antti Airola
Pairwise learning is a specialized form of supervised learning that focuses on predicting outcomes for pairs of objects. In this work, we introduce SPaiK, a new scalable kernel learning method tailored for pairwise settings. Our approach preserves the expressive power of kernel methods while substantially reducing…
79relevance
07
PPubMed2 Jan 2026
Fast and Interpretable Machine Learning Modeling of Atmospheric Molecular Clusters
Lauri Seppäläinen, Jakub Kubečka, Jonas Elm, Kai R. Puolamäki
Machine Learning Modeling of Atmospheric Molecular Clusters Authors: Lauri Seppäläinen, Jakub Kubečka, Jonas Elm, Kai R. Puolamäki Understanding how atmospheric molecular clusters form and grow is key to resolving one of the biggest uncertainties in climate modeling: the formation of new aerosol particles. While…
79relevance
08
AarXiv8 May 2026
Characterizing and Correcting Effective Target Shift in Online Learning
Ziyan Li, Naoki Hiratani
Online learning from a stream of data is a defining feature of intelligence, yet modern machine learning systems often struggle in this setting, especially under distributional shift. To understand its basic properties, we study the relationship between online and offline learning in the context of kernel regression.…
78relevance
09
BbioRxiv19 Nov 2025
CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data
Anirban Chakraborty, Chloe Mattila, Debashis Ghosh, Brian Neelon + 1 more
High-throughput bulk and single-cell omics technologies enable comprehensive molecular profiling, yet identifying compact, biologically interpretable marker sets that distinguish cell types, conditions, or disease states remains challenging. Standard pipelines rely on univariate differential expression tests, which…
78relevance
10
CchemRxiv28 Dec 2025
Accurate and Robust Analysis of Molecular Kinetics with Random Features
Authors not listed
Metastable states and the conformational transitions in between them are key to understanding dynamical behaviour and function of large-scale molecular systems. By combining basic dimensionality reduction techniques with a state-of-the art approximation of the Koopman operator associated to molecular dynamics…
77relevance
11
AarXiv2 Dec 2025
Tensor Network Based Feature Learning Model
Albert Saiapin, Kim Batselier
Many approximations were suggested to circumvent the cubic complexity of kernelbased algorithms, allowing their application to large-scale datasets. One strategy is to consider the primal formulation of the learning problem by mapping the data to a higher-dimensional space using tensorproduct structured polynomial and…
76relevance
12
AarXiv27 Oct 2025
Manifold Approximation leads to Robust Kernel Alignment
Islam, Mohammad Tariqul, Liu, Du + 2 more
Centered kernel alignment (CKA) is a popular metric for comparing representations, determining equivalence of networks, and neuroscience research. However, CKA does not account for the underlying manifold and relies on numerous heuristics that cause it to behave differently at different scales of data. In this work, we…
76relevance
13
PPubMed16 Oct 2025
An Adapted Similarity Kernel and Generalized Convex Hull for Molecular Crystal Structure Prediction
Jennie Martin, Michele Ceriotti, Graeme M. Day
Kernel and Generalized Convex Hull for Molecular Crystal Structure Prediction Authors: Jennie Martin, Michele Ceriotti, Graeme M. Day We adapted an existing approach to identifying stabilizable crystal structures from prediction setsthe Generalized Convex Hull (GCH)to improve its application to molecular crystal…
76relevance
14
AarXiv16 Jan 2026
Quantum Kernel Machine Learning for Autonomous Materials Science
F. Adams, Daiwei Zhu, David Steuerman, A. Gilad Kusne + 1 more
Autonomous materials science, where active learning is used to navigate large compositional phase space, has emerged as a powerful vehicle to rapidly explore new materials. A crucial aspect of autonomous materials science is exploring new materials using as little data as possible. Gaussian process-based active…
75relevance
15
CchemRxiv29 Oct 2025
What drives property prediction for solid-state hydrogen storage? Data or smart features?
Authors not listed
Metal hydrides play a pivotal role in a wide range of applications, including hydrogen storage, compression, heat management, and catalysis, making them a central focus of interdisciplinary research spanning chemistry, materials science, and engineering. The performance of the metal hydride based systems is strongly…
75relevance
16
BbioRxiv30 Jul 2026
RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data
Souvik Seal, Anirban Chakraborty, Chloe Mattila, Mark Rubinstein + 4 more
High-throughput spatial omics technologies enable molecular profiling within intact tissue architecture, yet identifying concise, predictive, and biologically interpretable marker panels for cell types, tissue domains, and disease-associated tissue classes remains challenging. This limitation hinders the development of…
73relevance
17
BbioRxiv18 Mar 2026
Cerebellum-inspired Kernel for Efficient Out-of-Distribution Detection
Yaoduo Zhang, Jian Zhang, Yunliang Zang
Detecting novel stimuli is a fundamental neural function, yet its machine learning counterpart—out-of-distribution (OOD) detection—remains challenging, with models often making overconfident predictions on unseen inputs. Inspired by the strong pattern-separation capabilities of cerebellum-like circuits, we introduce a…
72relevance
18
PPubMed8 Nov 2025
Multipath cycleGAN for harmonization of paired and unpaired low‐dose lung computed tomography reconstruction kernels KRISHNAN et al.
Aravind R. Krishnan, Thomas Z. Li, Lucas W. Remedios, Michael E. Kim + 11 more
Kernel harmonization is an approach that mitigates systematic differences in quantitative measurements due to the variability of reconstruction kernels. The goal of kernel harmonization is to standardize the pixel noise in images reconstructed with different kernels to a common reference kernel, ensuring comparable and…
72relevance
19
PPubMed31 Jul 2026
Ensemble learning-based online sequential pre-interference extreme learning for concept drifting and class imbalanced data streams An online learning algorithm for class imbalance and concept drift
Yinjie Huang, Hui Wen, Qunhua Tang, Haitao Liu + 1 more
With the rapid development of data-driven technologies, real-time data streams not only exhibit concept drift but are also frequently accompanied by class imbalance problems. To address these challenges, this paper proposes an online sequential pre-interference layer extreme learning machine (OS-PIELM). The proposed…
68relevance
20
CchemRxiv23 Dec 2025
Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models
Authors not listed
Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published…
66relevance
21
BbioRxiv28 Feb 2026
scProfiterole: Clustering of Single-Cell Proteomic Data Using Graph Contrastive Learning via Spectral Filters
Mustafa Coşkun, Filipa Blasco Lopes, Pınar Kubilay Tolunay, Mark R. Chance + 1 more
Novel technologies for the acquisition of protein expression data at the single cell level are emerging rapidly. Although there exists a substantial body of computational algorithms and tools for the analysis of single cell gene expression (scRNAseq) data, tools for even basic tasks such as clustering or cell type…
65relevance
22
BbioRxiv25 Jun 2026
Overinflation and overconcentration: why Cauchy perturbation kernels are the right choice for ABC-SMC
Marc Sturrock, Vahid Shahrezaei
Approximate Bayesian computation sequential Monte Carlo (ABC-SMC) propagates its particles with a perturbation kernel, and with the standard Normal kernel it degrades sharply as the parameter dimension grows, a failure usually attributed to dimension itself. We show instead that it is governed by the quality of the…
65relevance
23
BbioRxiv19 Dec 2025
Explainable Active Learning Framework for Ligand Binding Affinity Prediction
Satya Pratik Srivastava, Rohan Gorantla, Sharath Krishna Chundru, Claire J.R. Winkelman + 2 more
Active learning (AL) prioritises which compounds to measure next for protein–ligand affinity when assay or simulation budgets are limited. We present an explainable AL framework built on Gaussian process regression and assess how molecular representations, covariance kernels, and acquisition policies affect enrichment…
64relevance
24
CchemRxiv18 Dec 2025
Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes
Authors not listed
Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…
56relevance
25
CchemRxiv21 Oct 2025
Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
53relevance

Results for Kernel learning

Maritime object classification with SAR imagery using quantum kernel methods

Adaptive Kernel Methods

Learning cardiac activation and repolarization times with operator learning Learning cardiac activation and repolarization times with operator learning

Support Vector Machine with a Scalable Quantum Kernel

Exploring novel semi-inner product reproducing Kernels in Banach space for robust Kernel methods Semi-inner product reproducing Kernels in Banach space for robust Kernel methods

Scalable Pairwise Kernel Learning with Stochastic Vec Trick

Fast and Interpretable Machine Learning Modeling of Atmospheric Molecular Clusters

Characterizing and Correcting Effective Target Shift in Online Learning

CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data

Accurate and Robust Analysis of Molecular Kinetics with Random Features

Tensor Network Based Feature Learning Model

Manifold Approximation leads to Robust Kernel Alignment

An Adapted Similarity Kernel and Generalized Convex Hull for Molecular Crystal Structure Prediction

Quantum Kernel Machine Learning for Autonomous Materials Science

What drives property prediction for solid-state hydrogen storage? Data or smart features?

RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data

Cerebellum-inspired Kernel for Efficient Out-of-Distribution Detection

Multipath cycleGAN for harmonization of paired and unpaired low‐dose lung computed tomography reconstruction kernels KRISHNAN et al.

Ensemble learning-based online sequential pre-interference extreme learning for concept drifting and class imbalanced data streams An online learning algorithm for class imbalance and concept drift

Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models

scProfiterole: Clustering of Single-Cell Proteomic Data Using Graph Contrastive Learning via Spectral Filters

Overinflation and overconcentration: why Cauchy perturbation kernels are the right choice for ABC-SMC

Explainable Active Learning Framework for Ligand Binding Affinity Prediction

Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Maritime object classification with SAR imagery using quantum kernel methods

Adaptive Kernel Methods

Learning cardiac activation and repolarization times with operator learning Learning cardiac activation and repolarization times with operator learning

Support Vector Machine with a Scalable Quantum Kernel

Exploring novel semi-inner product reproducing Kernels in Banach space for robust Kernel methods Semi-inner product reproducing Kernels in Banach space for robust Kernel methods

Scalable Pairwise Kernel Learning with Stochastic Vec Trick

Fast and Interpretable Machine Learning Modeling of Atmospheric Molecular Clusters

Characterizing and Correcting Effective Target Shift in Online Learning

CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data

Accurate and Robust Analysis of Molecular Kinetics with Random Features

Tensor Network Based Feature Learning Model

Manifold Approximation leads to Robust Kernel Alignment

An Adapted Similarity Kernel and Generalized Convex Hull for Molecular Crystal Structure Prediction

Quantum Kernel Machine Learning for Autonomous Materials Science

What drives property prediction for solid-state hydrogen storage? Data or smart features?

RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data

Cerebellum-inspired Kernel for Efficient Out-of-Distribution Detection

Multipath cycleGAN for harmonization of paired and unpaired low‐dose lung computed tomography reconstruction kernels KRISHNAN et al.

Ensemble learning-based online sequential pre-interference extreme learning for concept drifting and class imbalanced data streams An online learning algorithm for class imbalance and concept drift

Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models

scProfiterole: Clustering of Single-Cell Proteomic Data Using Graph Contrastive Learning via Spectral Filters

Overinflation and overconcentration: why Cauchy perturbation kernels are the right choice for ABC-SMC

Explainable Active Learning Framework for Ligand Binding Affinity Prediction

Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics