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Support vector machine (SVM) is a powerful classification method that has achieved great success in many fields. Since its performance can be seriously impaired by redundant covariates, model selection techniques are widely used for SVM…

Machine Learning · Statistics 2022-07-25 Chaoxia Yuan , Chao Ying , Zhou Yu , Fang Fang

In recent years, machine learning (ML) has become a key enabling technology for the sciences and industry. Especially through improvements in methodology, the availability of large databases and increased computational power, today's ML…

Artificial Intelligence · Computer Science 2019-09-27 Wojciech Samek , Klaus-Robert Müller

The support vector machine (SVM) was originally designed for binary classifications. A lot of effort has been put to generalize the binary SVM to multiclass SVM (MSVM) which are more complex problems. Initially, MSVMs were solved by…

Machine Learning · Statistics 2015-12-01 Yangyang Xu , Ioannis Akrotirianakis , Amit Chakraborty

The recent physical realisation of quantum computers with dozens to hundreds of noisy qubits has given birth to an intense search for useful applications of their unique capabilities. One area that has received particular attention is…

Quantum Physics · Physics 2023-09-15 Maxwell T. West , Martin Sevior , Muhammad Usman

The greatest demand for today's computing is machine learning. This paper analyzes three machine learning algorithms: transformers, spatial convolution, and FFT. The analysis is novel in three aspects. First, it measures the cost of memory…

Systems and Control · Electrical Eng. & Systems 2023-12-25 Chen Ding , Christopher Kanan , Dylan McKellips , Toranosuke Ozawa , Arian Shahmirza , Wesley Smith

We classify all mutation-finite quivers with real weights. We show that every finite mutation class not originating from an integer skew-symmetrizable matrix has a geometric realization by reflections. We also explore the structure of…

Combinatorics · Mathematics 2022-05-04 Anna Felikson , Pavel Tumarkin

We derive an algorithm for mutating quivers of 2-CY tilted algebras that have loops and 2-cycles, under certain specific conditions. Further, we give the classification of the 2-CY tilted algebras coming from standard algebraic 2-CY…

Representation Theory · Mathematics 2010-04-26 Marco Angel Bertani-Økland , Steffen Oppermann

Classification of galaxy morphology is a challenging but meaningful task for the enormous amount of data produced by the next-generation telescope. By introducing the adaptive polar coordinate transformation, we develop a rotationally…

Astrophysics of Galaxies · Physics 2023-01-11 G. W. Fang , S. Ba , Y. Z. Gu , Z. S. Lin , Y. J. Hou , C. X. Qin , C. C. Zhou , J. Xu , Y. Dai , J. Song , X. Kong

Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use…

Computer Vision and Pattern Recognition · Computer Science 2021-09-21 Mahabubul Alam , Satwik Kundu , Rasit Onur Topaloglu , Swaroop Ghosh

Quantum machine learning (QML) is the spearhead of quantum computer applications. In particular, quantum neural networks (QNN) are actively studied as the method that works both in near-term quantum computers and fault-tolerant quantum…

Quantum Physics · Physics 2022-09-07 Kouhei Nakaji , Hiroyuki Tezuka , Naoki Yamamoto

Quantum Machine Learning (QML) is considered to be one of the most promising applications of near term quantum devices. However, the optimization of quantum machine learning models presents numerous challenges arising from the imperfections…

Machine Learning · Computer Science 2022-05-17 Owen Lockwood

Machine learning, deep learning, has been accelerating computational physics, which has been used to simulate systems on a lattice. Equivariance is essential to simulate a physical system because it imposes a strong induction bias for the…

High Energy Physics - Lattice · Physics 2023-10-23 Akio Tomiya , Yuki Nagai

The emergence of machine learning (ML) has led to a transformative shift in software techniques and guidelines for building software applications that support data analysis process activities such as data ingestion, modeling, and…

Software Engineering · Computer Science 2025-01-03 Cristina Tavares , Nathalia Nascimento , Paulo Alencar , Donald Cowan

The meteoric rise of artificial intelligence in recent years has seen machine learning methods become ubiquitous in modern science, technology, and industry. Concurrently, the emergence of programmable quantum computers, coupled with the…

Quantum Physics · Physics 2025-06-17 Muhammad Usman

First principles based exploration of chemical space deepens our understanding of chemistry, and might help with the design of new materials or experiments. Due to the computational cost of quantum chemistry methods and the immens number of…

Chemical Physics · Physics 2020-08-18 Bing Huang , O. Anatole von Lilienfeld

We give a precise definition of mutation of skew symmetrizable matrices over group rings and relate it to folding and mutation of quivers with symmetries. These matrices can have non-zero diagonal entries and we explain a mutation rule in…

Combinatorics · Mathematics 2026-01-23 Dani Kaufman , Carmen Alves Sabin

Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle physicists conduct searches and measurements as a versatile…

High Energy Physics - Experiment · Physics 2024-10-01 Javier M. Duarte

The exploration of planetary bodies in our Solar system and beyond relies on the processing and interpretation of large, spatio-temporally inconsistent, and heterogeneous datasets. Recent advances in machine learning (ML) provide…

We introduce new methods for phylogenetic tree quartet construction by using machine learning to optimize the power of phylogenetic invariants. Phylogenetic invariants are polynomials in the joint probabilities which vanish under a model of…

Populations and Evolution · Quantitative Biology 2007-05-23 Nicholas Eriksson , Yuan Yao

The support vector machine (SVM) is a powerful and widely used classification algorithm. This paper uses the Karush-Kuhn-Tucker conditions to provide rigorous mathematical proof for new insights into the behavior of SVM. These insights…

Machine Learning · Statistics 2018-10-11 Iain Carmichael , J. S. Marron
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