Results 11 to 20 of about 93,670 (235)
A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groups [PDF]
, 2022 In this paper, we introduce a novel methodology to model rating transitions with a stochastic process. To introduce stochastic processes, whose values are valid rating matrices, we noticed the geometric properties of stochastic matrices and its link to matrix Lie groups. We give a gentle introduction to this topic and demonstrate how Itô-SDEs in R will
Kamm, Kevin, Muniz, Michelle
openaire +3 more sources
Group-Invariant Quantum Machine Learning [PDF]
PRX Quantum, 2022 Quantum machine learning (QML) models are aimed at learning from data encoded in quantum states. Recently, it has been shown that models with little to no inductive biases (i.e., with no assumptions about the problem embedded in the model) are likely to ...
Martín Larocca +5 more
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Machine learning of knot topology in non-Hermitian band braids [PDF]
Communications PhysicsThe deep connection among braids, knots and topological physics has provided valuable insights into studying topological states in various physical systems. However, identifying distinct braid groups and knot topology embedded in non-Hermitian systems is
Jiangzhi Chen +4 more
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MOOC Dropout Prediction via a Dilated Convolutional Attention Network with Lie Group Features
InformaticsMassive open online courses (MOOCs) represent an innovative online learning paradigm that has garnered considerable popularity in recent years, attracting a multitude of learners to MOOC platforms due to their accessible and adaptable instructional ...
Yinxu Liu +3 more
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Data Obfuscation for Privacy-Preserving Machine Learning Using Quantum Symmetry Properties
Big Data and Cognitive ComputingThis study introduces a data obfuscation technique that leverages the exponential map of Lie-group generators. Originating from quantum machine learning frameworks, the method injects controlled noise into these generators, deliberately breaking symmetry
Sebastian Raubitzek +3 more
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Filtering and Machine Learning on Riemannian Manifolds and Lie Groups
Signal ProcessingSamy Labsir +3 more
+4 more sources
SCENE CLASSIFICATION BASED ON THE INTRINSIC MEAN OF LIE GROUP [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Remote Sensing scene classification aims to identify semantic objects with similar characteristics from high resolution images. Even though existing methods have achieved satisfactory performance, the features used for classification modeling are still ...
C. Xu, G. Zhu, K. Yang
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Deep learning symmetries and their Lie groups, algebras, and subalgebras from first principles
Machine Learning: Science and Technology, 2023 We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset. We use fully connected neural networks to model the symmetry transformations and the corresponding generators ...
Roy T Forestano +5 more
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Identifying the group-theoretic structure of machine-learned symmetries
Physics Letters B, 2023 Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic, these techniques postpone the identification of the discovered symmetries to a later stage.
Roy T. Forestano +5 more
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On the Applicability of Quantum Machine Learning
Entropy, 2023 In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the variational quantum circuit and the quantum kernel estimator (QKE).
Sebastian Raubitzek, Kevin Mallinger
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