Tootfinder

Exploratory Landscape Analysis for Mixed-Variable Problems
Raphael Patrick Prager, Heike Trautmann
https://arxiv.org/abs/2402.16467 https://arxiv.org/pdf/2402.16467
arXiv:2402.16467v1 Announce Type: new
Abstract: Exploratory landscape analysis and fitness landscape analysis in general have been pivotal in facilitating problem understanding, algorithm design and endeavors such as automated algorithm selection and configuration. These techniques have largely been limited to search spaces of a single domain. In this work, we provide the means to compute exploratory landscape features for mixed-variable problems where the decision space is a mixture of continuous, binary, integer, and categorical variables. This is achieved by utilizing existing encoding techniques originating from machine learning. We provide a comprehensive juxtaposition of the results based on these different techniques. To further highlight their merit for practical applications, we design and conduct an automated algorithm selection study based on a hyperparameter optimization benchmark suite. We derive a meaningful compartmentalization of these benchmark problems by clustering based on the used landscape features. The identified clusters mimic the behavior the used algorithms exhibit. Meaning, the different clusters have different best performing algorithms. Finally, our trained algorithm selector is able to close the gap between the single best and the virtual best solver by 57.5% over all benchmark problems.

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System size scaling of triangularity effects on global temperature gradient-driven gyrokinetic simulations
Giovanni Di Giannatale, Alberto Bottino, Stephan Brunner, Moahan Murugappan, Laurent Villard
https://arxiv.org/abs/2402.16216

System size scaling of triangularity effects on global temperature gradient-driven gyrokinetic simulations
In this work, we explore the triangularity effects on turbulent transport employing global gyrokinetic simulations performed with the ORB5 code. Numerous experiments on the Tokamak à Configuration Variable (TCV) and, more recently, on the DIII-D machine, have demonstrated superior confinement properties in L-mode of negative triangularity (NT) over positive triangularity (PT) configuration. This presents a particularly attractive scenario, as L-mode operation eliminates or significantly mitiga…

This https://arxiv.org/abs/2401.05647 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum Kernel Machine Learning With Continuous Variables
The popular qubit framework has dominated recent work on quantum kernel machine learning, with results characterising expressivity, learnability and generalisation. As yet, there is no comparative framework to understand these concepts for continuous variable (CV) quantum computing platforms. In this paper we represent CV quantum kernels as closed form functions and use this representation to provide several important theoretical insights. We derive a general closed form solution for all CV qua…

An Introduction to Complex Random Tensors
Divyanshu Pandey, Alexis Decurninge, Harry Leib
https://arxiv.org/abs/2404.15170 https://ar…

An Introduction to Complex Random Tensors
This work considers the notion of random tensors and reviews some fundamental concepts in statistics when applied to a tensor based data or signal. In several engineering fields such as Communications, Signal Processing, Machine learning, and Control systems, the concepts of linear algebra combined with random variables have been indispensable tools. With the evolution of these subjects to multi-domain communication systems, multi-way signal processing, high dimensional data analysis, and multi…

This https://arxiv.org/abs/2305.13982 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Toward stochastic neural computing
The highly irregular spiking activity of cortical neurons and behavioral variability suggest that the brain could operate in a fundamentally probabilistic way. Mimicking how the brain implements and learns probabilistic computation could be a key to developing machine intelligence that can think more like humans. In this work, we propose a theory of stochastic neural computing (SNC) in which streams of noisy inputs are transformed and processed through populations of nonlinearly coupled spiking…

This https://arxiv.org/abs/2303.01840 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Learning-based Position and Stiffness Feedforward Control of Antagonistic Soft Pneumatic Actuators using Gaussian Processes
Variable stiffness actuator (VSA) designs are manifold. Conventional model-based control of these nonlinear systems is associated with high effort and design-dependent assumptions. In contrast, machine learning offers a promising alternative as models are trained on real measured data and nonlinearities are inherently taken into account. Our work presents a universal, learning-based approach for position and stiffness control of soft actuators. After introducing a soft pneumatic VSA, the model …

This https://arxiv.org/abs/2312.03263 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment
Optimal decision-making presents a significant challenge for autonomous systems operating in uncertain, stochastic and time-varying environments. Environmental variability over time can significantly impact the system's optimal decision making strategy for mission completion. To model such environments, our work combines the previous notion of Time-Varying Markov Decision Processes (TVMDP) with partial observability and introduces Time-Varying Partially Observable Markov Decision Processes (TV-…

This https://arxiv.org/abs/2401.05647 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum Kernel Machine Learning With Continuous Variables
The popular qubit framework has dominated recent work on quantum kernel machine learning, with results characterising expressivity, learnability and generalisation. As yet, there is no comparative framework to understand these concepts for continuous variable (CV) quantum computing platforms. In this paper we represent CV quantum kernels as holomorphic functions and use this representation to provide several important theoretical insights. We derive a general closed form solution for all CV qua…

Piecewise Polynomial Tensor Network Quantum Feature Encoding
Mazen Ali, Matthias Kabel
https://arxiv.org/abs/2402.07671 https://arxiv…

Piecewise Polynomial Tensor Network Quantum Feature Encoding
This work introduces a novel method for embedding continuous variables into quantum circuits via piecewise polynomial features, utilizing low-rank tensor networks. Our approach, termed Piecewise Polynomial Tensor Network Quantum Feature Encoding (PPTNQFE), aims to broaden the applicability of quantum algorithms by incorporating spatially localized representations suited for numerical applications like solving partial differential equations and function regression. We demonstrate the potential o…