Implementation of a Learning-Based Explicit Reference Governor for Constrained Control of a UAV

Higgins, Michael; Burlion, Laurent

doi:10.2514/6.2022-2042

Cited by 6 publications

(12 citation statements)

References 20 publications

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“…The regularization loss, on the other hand, is given by the Kullback-Leibler divergence of the latent representation and a standard normal distribution. This encourages the latent representation to be smooth and continuous, and moreover aims at having latent variables represent independent generative factors [22,25]. For a single data point this loss can be expressed as…”

Section: Vaesmentioning

confidence: 99%

“…where i runs over the N latent variables. The hyperparameter β in equation ( 5) controls the impact of regularization on the overall optimization objective, regulating the trade-off between the effective encoding capacity of the latent space and the statistical independence of individual latent variables in the learned representation [25].…”

Section: Vaesmentioning

confidence: 99%

“…A VAE performs dimensionality reduction [23], compressing an input into a smaller dimension called the latent space, and then attempts to reconstruct the input from that latent representation. Originally proposed as a generative model for image data, this architecture has proven to be capable of extracting ground-truth generative factors (underlying feature of the data which captures a distinct attribute or characteristic) from highly complex feature spaces and representing them in a human interpretable manner [24,25]. In particular, the so-called β-VAE introduces a regularization hyperparameter which encourages independent latent variables, leading to more interpretable representations [25].…”

Section: Introductionmentioning

confidence: 99%

“…Originally proposed as a generative model for image data, this architecture has proven to be capable of extracting ground-truth generative factors (underlying feature of the data which captures a distinct attribute or characteristic) from highly complex feature spaces and representing them in a human interpretable manner [24,25]. In particular, the so-called β-VAE introduces a regularization hyperparameter which encourages independent latent variables, leading to more interpretable representations [25]. Thus, we conduct a hyperparameter search on β, and our results reveal that the smallest latent representation the model can learn is interpretable and captures entanglement properties.…”

Section: Introductionmentioning

confidence: 99%

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Explainable representation learning of small quantum states

Frohnert,

van Nieuwenburg

2024

Mach. Learn.: Sci. Technol.

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Unsupervised machine learning models build an internal representation of their training datawithout the need for explicit human guidance or feature engineering. This learned representationprovides insights into which features of the data are relevant for the task at hand. In the context ofquantum physics, training models to describe quantum states without human intervention offers apromising approach to gaining insight into how machines represent complex quantum states. Theability to interpret the learned representation may offer a new perspective on non-trivial featuresof quantum systems and their efficient representation. We train a generative model on two-qubitdensity matrices generated by a parameterized quantum circuit. In a series of computational ex-periments, we investigate the learned representation of the model and its internal understanding ofthe data. We observe that the model learns an interpretable representation which relates the quan-tum states to their underlying entanglement characteristics. In particular, our results demonstratethat the latent representation of the model is directly correlated with the entanglement measureconcurrence. The insights from this study represent proof of concept towards interpretable ma-chine learning of quantum states. Our approach offers insight into how machines learn to representsmall-scale quantum systems autonomously.

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Section: Vaesmentioning

confidence: 99%

Section: Vaesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Explainable representation learning of small quantum states

Frohnert,

van Nieuwenburg

2024

Mach. Learn.: Sci. Technol.

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show abstract

“…The minimization of this loss function ensures that the output will be as close as possible to the input, while the values of the latent space closely follow the chosen distribution. In addition, one can add a hyperparameter β [45] to control the importance of each term in the loss. The loss function can be written as:…”

Section: Convae Loss Function and Evaluation Metricmentioning

confidence: 99%

LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows

Orzari,

Chernyavskaya,

Cobe

et al. 2023

Mach. Learn.: Sci. Technol.

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In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. However, because of the upcoming high-luminosity upgrade of the LHC, there will not be enough computational power or time to match the amount of needed simulated data using MC methods. An alternative approach under study is the usage of machine learning generative methods to fulfill that task. Since the most common final-state objects of high-energy proton collisions are hadronic jets, which are collections of particles collimated in a given region of space, this work aims to develop a convolutional variational autoencoder (ConVAE) for the generation of particle-based LHC hadronic jets. Given the ConVAE's limitations, a normalizing flow (NF) network is coupled to it in a two-step training process, which shows improvements on the results for the generated jets. The ConVAE+NF network is capable of generating a jet in 18.30 ± 0.04 μs, making it one of the fastest methods for this task up to now.

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Protecting Systems from Violating Constraints Using Reference Governors

Kolmanovsky

Li²

2022

SN COMPUT. SCI.

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Implementation of a Learning-Based Explicit Reference Governor for Constrained Control of a UAV

Cited by 6 publications

References 20 publications

Explainable representation learning of small quantum states

Explainable representation learning of small quantum states

LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows

Protecting Systems from Violating Constraints Using Reference Governors

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