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14 papers · ranked by Valyu relevance

01
AarXiv2023
Quantum-Classical Multiple Kernel Learning
Ara Ghukasyan, Jack S. Baker, Oktay Göktaş, Juan Carrasquilla + 1 more
'Santosh Kumar Radha'] As quantum computers become increasingly practical, so does the prospect of using quantum computation to improve upon traditional algorithms. Kernel methods in machine learning is one area where such improvements could be realized in the near future. Paired with kernel methods like support-vector…
79relevance
02
AarXiv24 Nov 2022
Stein Variational Gradient Descent with Multiple Kernels
Qingzhong Ai, Shiyu Liu, Lirong He, Zenglin Xu
Background: Stein variational gradient descent (SVGD) and its variants have shown promising successes in approximate inference for complex distributions. In practice, we notice that the kernel used in SVGD-based methods has a decisive effect on the empirical performance. Radial basis function (RBF) kernel with median…
78relevance
03
AarXiv26 Mar 2024
MEP: Multiple Kernel Learning Enhancing Relative Positional Encoding Length Extrapolation
Weiguo Gao
Length Extrapolation Authors: ['Weiguo Gao'] When the predicted sequence length exceeds the length seen during training, the transformer's inference accuracy diminishes. To address these challenges, the ability to generalize or extrapolate to longer sequence lengths has garnered increased attention. Existing relative…
77relevance
04
AarXiv2 Feb 2024
Kernel correlation–dissimilarity for Multiple Kernel k-Means clustering
Rina Su, Yu Guo, Caiying Wu, Qiyu Jin + 1 more
The main objective of the Multiple Kernel k-Means (MKKM) algorithm is to extract non-linear information and achieve optimal clustering by optimizing base kernel matrices. Current methods enhance information diversity and reduce redundancy by exploiting interdependencies among multiple kernels based on correlations or…
74relevance
05
AarXiv10 Oct 2022
Multiple Kernel Clustering with Dual Noise Minimization
Junpu Zhang, Liang Li, Siwei Wang, Jiyuan Liu + 3 more
'Xinwang Liu' 'En Zhu'] Clustering is a representative unsupervised method widely applied in multi-modal and multi-view scenarios. Multiple kernel clustering (MKC) aims to group data by integrating complementary information from base kernels. As a representative, late fusion MKC first decomposes the kernels into…
73relevance
06
AarXiv26 May 2024
Fast Asymmetric Factorization for Large Scale Multiple Kernel Clustering
Yan Chen Yan Chen, Liang Du, Lei Duan
—Kernel methods are extensively employed for nonlinear data clustering, yet their effectiveness heavily relies on selecting suitable kernels and associated parameters, posing challenges in advance determination. In response, Multiple Kernel Clustering (MKC) has emerged as a solution, allowing the fusion of information…
70relevance
07
AarXiv16 May 2025
SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes
Nima Negarandeh, Carlos Mora, Ramin Bostanabad
Gaussian processes (GPs) are powerful probabilistic models that define flexible priors over functions, offering strong interpretability and uncertainty quantification. However, GP models often rely on simple, stationary kernels which can lead to suboptimal predictions and miscalibrated uncertainty estimates, especially…
70relevance
08
CchemRxiv28 Aug 2025
An Adapted Similarity Kernel and Generalised Convex Hull for Molecular Crystal Structure Prediction
Authors not listed
We adapted an existing approach to identifying stabilisable crystal structures from prediction sets - the Generalised Convex Hull (GCH) - to improve its application to molecular crystal structures. This was achieved by modifying the Smooth Overlap of Atomic Positions (SOAP) kernel to define the similarity of molecular…
63relevance
09
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…
63relevance
10
CchemRxiv8 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…
62relevance
11
CchemRxiv10 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
59relevance
12
CchemRxiv21 Sep 2023
Machine Learning of Reactive Potentials
Yinuo Yang, Shuhao Zhang, Kavindri Ranasinghe, Olexandr Isayev + 1 more
In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological and material sciences. The construction and training of MLPs enables fast and accurate simulations and analysis on thermodynamic and kinetic properties. This review focuses on the applications of…
56relevance
13
CchemRxiv25 Mar 2024
Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices
Martin Seifrid, Stanley Lo, Dylan Choi, Gary Tom + 12 more
Martin Seifrid 1 , Stanley Lo 2 , Dylan G. Choi 3 , Gary Tom 2 , My Linh Le 3 , Kunyu Li 3 , Rahul Sankar 3 , Hoai-Thanh Vuong 3 , Hiba Wakidi 3 , Ahra Yi 3 , Ziyue Zhu 3 , Nora Schopp 3 , Aaron Peng 3 , Benjamin Luginbuhl 3 , Thuc-Quyen Nguyen 3 , Alán Aspuru-Guzik 2
56relevance
14
CchemRxiv15 Jun 2022
Krein Support Vector Machine Classification of Antimicrobial Peptides
Joseph Redshaw, Darren Ting, Alex Brown, Jonathan Hirst + 1 more
Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and timeconsuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating…
56relevance

Results for multiple kernel function

Quantum-Classical Multiple Kernel Learning

Stein Variational Gradient Descent with Multiple Kernels

MEP: Multiple Kernel Learning Enhancing Relative Positional Encoding Length Extrapolation

Kernel correlation–dissimilarity for Multiple Kernel k-Means clustering

Multiple Kernel Clustering with Dual Noise Minimization

Fast Asymmetric Factorization for Large Scale Multiple Kernel Clustering

SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes

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

Accurate and Robust Analysis of Molecular Kinetics with Random Features

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Machine Learning of Reactive Potentials

Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices

Krein Support Vector Machine Classification of Antimicrobial Peptides

Quantum-Classical Multiple Kernel Learning

Stein Variational Gradient Descent with Multiple Kernels

MEP: Multiple Kernel Learning Enhancing Relative Positional Encoding Length Extrapolation

Kernel correlation–dissimilarity for Multiple Kernel k-Means clustering

Multiple Kernel Clustering with Dual Noise Minimization

Fast Asymmetric Factorization for Large Scale Multiple Kernel Clustering

SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes

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

Accurate and Robust Analysis of Molecular Kinetics with Random Features

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Machine Learning of Reactive Potentials

Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices

Krein Support Vector Machine Classification of Antimicrobial Peptides