Results 1 to 10 of about 1,150,989 (314)
Gradient methods with memory [PDF]
Optimization Methods and Software, 2021 In this paper, we consider gradient methods for minimizing smooth convex functions, which employ the information obtained at the previous iterations in order to accelerate the convergence towards the optimal solution. This information is used in the form of a piece-wise linear model of the objective function, which provides us with much better ...
Nesterov, Yurii, Florea, Mihai I.
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Gradient Agreement Hinders the Memorization of Noisy Labels
Applied Sciences, 2023 The performance of deep neural networks (DNNs) critically relies on high-quality annotations, while training DNNs with noisy labels remains challenging owing to their incredible capacity to memorize the entire training set.
Shaotian Yan +3 more
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IET Smart Grid, 2020 The potential gradient method is proposed for system scalability of pulsed power networks. The pulsed power network is already proposed for the seamless integration of distributed generations. In this network, each power transmission is decomposed into a
Hisayoshi Sugiyama
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IET Electric Power Applications, 2021 Early fault detection in an induction motor is the need of modern industries for minimal downtime and maximum production. A learning technique known as the Convolutional Neural network (CNN) provides automated and reliable feature extraction and ...
Prashant Kumar, Ananda Shankar Hati
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Interactive statistical computer program for multiple non-linear curves fitting using stochastic algorithms [PDF]
Computational Ecology and Software, 2021 An interactive computer program for multiple nonlinear curves fitting has been developed in this work. Several optimization algorithms have been implemented in this software for solving constrained and unconstrained nonlinear optimization models in order
Muhammad Tlas +2 more
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Differentially Quantized Gradient Methods
IEEE Transactions on Information Theory, 2022 Consider the following distributed optimization scenario. A worker has access to training data that it uses to compute the gradients while a server decides when to stop iterative computation based on its target accuracy or delay constraints. The server receives all its information about the problem instance from the worker via a rate-limited noiseless ...
Chung-Yi Lin +2 more
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Optimal Design and Comparison of High-Frequency Resonant and Non-Resonant Rotary Transformers
Energies, 2020 This paper concerns the optimal design and comparative analysis of resonant and non-resonant high-frequency GaN-based rotating transformers. A multi-physical design approach is employed, in which magnetic, electrical, and thermal models are coupled.
Koen Bastiaens +3 more
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IEEE Access, 2023 As the performance of computing devices such as graphics processing units (GPUs) has improved dramatically, many deep neural network models, especially convolutional neural networks (CNNs), have been widely applied to various applications such as image ...
Kyung Soo Kim, Yong Suk Choi
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Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization
Frontiers in Bioengineering and Biotechnology, 2023 Majority of modern techniques for creating and optimizing the geometry of medical devices are based on a combination of computer-aided designs and the utility of the finite element method This approach, however, is limited by the number of geometries ...
Viacheslav V. Danilov +7 more
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IEEE Access, 2020 The Fourier-based gradient field integration method can efficiently reconstruct transparent surfaces from the measured gradient data in the Shape-from-polarization method.
Zhuang Sun +5 more
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