Results 11 to 20 of about 21,386 (201)
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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A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groups
CoRR, 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
Kevin Kamm, Michelle Muniz
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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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Rating Triggers for Collateral-Inclusive XVA via Machine Learning and SDEs on Lie Groups
CoRR, 2022 In this paper, we model the rating process of an entity by using a geometrical approach. We model rating transitions as an SDE on a Lie group. Specifically, we focus on calibrating the model to both historical data (rating transition matrices) and market data (CDS quotes) and compare the most popular choices of changes of measure to switch from the ...
Kevin Kamm, Michelle Muniz
openaire +2 more sources
Multi-Stage Meta-Learning for Few-Shot with Lie Group Network Constraint
Entropy, 2020 Deep learning has achieved many successes in different fields but can sometimes encounter an overfitting problem when there are insufficient amounts of labeled samples.
Fang Dong, Li Liu, Fanzhang Li
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HOMO–LUMO Gaps and Molecular Structures of Polycyclic Aromatic Hydrocarbons in Soot Formation
Frontiers in Mechanical Engineering, 2021 A large number of PAH molecules is collected from recent literature. The HOMO-LUMO gap value of PAHs was computed at the level of B3LYP/6-311+G (d,p). The gap values lie in the range of 0.64–6.59 eV.
Yabei Xu +3 more
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Group-Invariant Quantum Machine Learning
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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A Method for Calculating Network System Security Risk Based on a Lie Group
IEEE Access, 2019 Traditionally, network risk assessment uses a statistical computation method. This paper proposes Lie group kinematics to describe the feature space of the attack behavior. A matrix composed of indicators and topologies in a network system is mapped to a
Xiaolin Zhao +4 more
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