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Detecting Adversarial Examples Using Surrogate Models
Machine Learning and Knowledge Extraction, 2023 Deep Learning has enabled significant progress towards more accurate predictions and is increasingly integrated into our everyday lives in real-world applications; this is true especially for Convolutional Neural Networks (CNNs) in the field of image ...
Borna Feldsar +2 more
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Pre-training method in the tasks of obtaining surrogate models of gas turbine units for gas turbine electric power stations [PDF]
E3S Web of Conferences, 2023 This article focuses on the application of pre-training methods in the task of synthesizing surrogate models. The article emphasizes that pre-training significantly improves the accuracy of surrogate models and speeds up their creation process.
Kilin Grigory +3 more
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Mathematics, 2023 In recent years, a variety of data-driven evolutionary algorithms (DDEAs) have been proposed to solve time-consuming and computationally intensive optimization problems.
Zongliang Guo +3 more
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Detecting and Isolating Adversarial Attacks Using Characteristics of the Surrogate Model Framework
Applied Sciences, 2023 The paper introduces a novel framework for detecting adversarial attacks on machine learning models that classify tabular data. Its purpose is to provide a robust method for the monitoring and continuous auditing of machine learning models for the ...
Piotr Biczyk, Łukasz Wawrowski
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Large-Scale Expensive Optimization with a Switching Strategy
Complex System Modeling and Simulation, 2022 Some optimization problems in scientific research, such as the robustness optimization for the Internet of Things and the neural architecture search, are large-scale in decision space and expensive for objective evaluation.
Mai Sun +4 more
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Grid Enabled Surrogate Modeling [PDF]
, 2009 The simulation and optimization of complex systems is a very time consuming and computationally intensive task. Therefore, global surrogate modeling methods are often used for the efficient exploration of the design space, as they reduce the number of simulations needed.
Gorissen, Dirk +3 more
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Linear and nonlinear time series analysis of the black hole candidate Cygnus X-1 [PDF]
, 1999 We analyze the variability in the X-ray lightcurves of the black hole candidate Cygnus X-1 by linear and nonlinear time series analysis methods. While a linear model describes the over-all second order properties of the observed data well, surrogate data
Belloni, T. +7 more
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Classical Surrogates for Quantum Learning Models
Physical Review Letters, 2023 The advent of noisy intermediate-scale quantum computers has put the search for possible applications to the forefront of quantum information science. One area where hopes for an advantage through near-term quantum computers are high is quantum machine learning, where variational quantum learning models based on parametrized quantum circuits are ...
Franz J. Schreiber +2 more
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Surrogate modeling of RF circuit blocks [PDF]
, 2010 Surrogate models are a cost-effective replacement for expensive computer simulations in design space exploration. Literature has already demonstrated the feasibility of accurate surrogate models for single radio frequency (RF) and microwave devices ...
Croon, Jeroen A +3 more
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Versatile surrogate models for IC buffers [PDF]
, 2012 In previous papers [1,2] the authors have investigated the use of Volterra series in the identification of IC buffer macro-models. While the approach benefited from some of the inherent qualities of Volterra series it preserved the two-state paradigm of ...
Canavero, Flavio +5 more
core +3 more sources