Search Results - surrogate gradient algorithm

Comments on “Surrogate Gradient Algorithm for Lagrangian Relaxation” by Chang, T. S.

“…This note presents not only a surrogate subgradient method, but also a framework of surrogate subgradient methods…”
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On the Surrogate Gradient Algorithm for Lagrangian Relaxation by Sun, T., Zhao, Q. C., Luh, P. B.

“… Based on it, the penalty surrogate subgradient algorithm was further developed to address the homogenous solution issue (Guan et al.: J. Optim. Theory Appl. 113, 65-82, 2002; Zhai et al.: IEEE Trans. Power Syst…”
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Surrogate Gradient Algorithm for Lagrangian Relaxation by Zhao, X., Luh, P. B., Wang, J.

“… In the method, all subproblems must be solved optimally to obtain a subgradient direction. In this paper, the surrogate subgradient method is developed, where a proper direction can be obtained without solving optimally all the subproblems…”
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Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE) by Kaiser, Jacques, Mostafa, Hesham, Neftci, Emre

“… Learning algorithms that approximate gradient backpropagation using local error functions can overcome this challenge…”
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On the Surrogate Gradient Algorithm forLagrangian Relaxation by Sun, T, Zhao, Q C, Luh, P B

“… Based on it, the penalty surrogate subgradient algorithm was further developed to address the homogenous solution issue (Guan et al.: J. Optim. Theory Appl. 113, 65-82, 2002; Zhai et al.: IEEE Trans. Power Syst…”
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Surrogate gradients for analog neuromorphic computing by Cramer, Benjamin, Billaudelle, Sebastian, Kanya, Simeon, Leibfried, Aron, Grübl, Andreas, Karasenko, Vitali, Pehle, Christian, Schreiber, Korbinian, Stradmann, Yannik, Weis, Johannes, Schemmel, Johannes, Zenke, Friedemann

“… Surrogate gradient learning has emerged as a promising training strategy for spiking networks, but its applicability for analog neuromorphic systems has not been…”
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Directly training temporal Spiking Neural Network with sparse surrogate gradient by Li, Yang, Zhao, Feifei, Zhao, Dongcheng, Zeng, Yi

“… The surrogate gradient (SG) algorithm has recently enabled spiking neural networks to shine in neuromorphic hardware…”
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The surrogate gradient algorithm for Lagrangian relaxation method by Xing Zhao, Luh, P.B., Jihua Wang

“… Numerical results show that the interleaved subgradient method converges faster than the subgradient method, though algorithm convergence was not established…”
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Supply chain networks design with multi-mode demand satisfaction policy by Ardalan, Zaniar, Karimi, Sajad, Naderi, B., Arshadi Khamseh, Alireza

“…•This paper deals with a supply chain network design with multi-mode demand.•The problem is mathematically formulated as mixed integer linear programming.•A…”
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Methodology based on spiking neural networks for univariate time-series forecasting by Lucas, Sergio, Portillo, Eva

“…–decoding algorithm with a Surrogate Gradient method as supervised training algorithm. In order to validate the generality of the presented methodology sine-wave, 3 UCI and 1 available real-world datasets are used…”
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A gradient-descent-like learning-based framework in surrogate-assisted evolutionary algorithms for expensive many-objective optimization by Sun, Chaoyi, Zhang, Bo, Sun, Hai, Feng, Rui

“…Surrogate-assisted evolutionary algorithms (SAEAs) commonly depend on traditional offspring generation methods such as simulated binary crossover and polynomial mutation, which often lead to suboptimal search efficiencies…”
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Supervised Learning in All FeFET-Based Spiking Neural Network: Opportunities and Challenges by Dutta, Sourav, Schafer, Clemens, Gomez, Jorge, Ni, Kai, Joshi, Siddharth, Datta, Suman

“…The two possible pathways towards artificial intelligence – (i) neuroscience-oriented neuromorphic computing (like spiking neural network SNN) and (ii)…”
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Safety Performance Boundary Identification of Highly Automated Vehicles: A Surrogate Model-Based Gradient Descent Searching Approach by Wang, Yiyun, Yu, Rongjie, Qiu, Shuhan, Sun, Jian, Farah, Haneen

“… A surrogate model was utilized to approximate the safety performance of HAV, and a gradient descent searching…”
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Gradual Surrogate Gradient Learning in Deep Spiking Neural Networks by Chen, Yi, Zhang, Silin, Ren, Shiyu, Qu, Hong

“… In addition, we design a gradual surrogate gradient learning algorithm to ensure that SNNs effectively back-propagate gradient information in the early stage of training and more accurate gradient…”
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Semi-surrogate modelling of droplets evaporation process via XGBoost integrated CFD simulations by Yan, Yihuan, Li, Xueren, Sun, Weijie, Fang, Xiang, He, Fajiang, Tu, Jiyuan

“… This study proposed a semi-surrogate model for CFD with integration of the cutting-edge ML algorithm, eXtreme Gradient Boosting (XGB…”
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Cloud tomographic retrieval algorithms. I: Surrogate minimization method by Doicu, Adrian, Doicu, Alexandru, Efremenko, Dmitry, Trautmann, Thomas

“…) the surrogate minimization method for solving the inverse problem has been designed. The retrieval algorithm uses regularization, accelerated projected gradient methods, and two types of surrogate functions…”
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Probe into the volumetric properties of binary mixtures: Essence of regression-based machine learning algorithms by Sharma, Anshu, Li, Li, Garg, Aman, seop Lee, Bong

“… Four different machine learning algorithms are employed for making the surrogate models, namely, Gradient Boosting Machine (GBM), Stacked Ensemble (SE), Random Forest (RF…”
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An Efficient Hybrid Multi-Objective Optimization Method Coupling Global Evolutionary and Local Gradient Searches for Solving Aerodynamic Optimization Problems by Cao, Fan, Tang, Zhili, Zhu, Caicheng, Zhao, Xin

“…) and a gradient-based surrogate-assisted multi-objective hybrid algorithm (GS-MOHA) are developed under this framework…”
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Surrogate models and mixtures of experts in aerodynamic performance prediction for aircraft mission analysis by Liem, Rhea P., Mader, Charles A., Martins, Joaquim R.R.A.

“… Second, we improve the kriging surrogate performance by including gradient information in the interpolation…”
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Surrogate-Model Accelerated Random Search algorithm for global optimization with applications to inverse material identification by Brigham, John C., Aquino, Wilkins

“… The methodology, referred to as the Surrogate-Model Accelerated Random Search (SMARS) algorithm, is a non-gradient based iterative application of a random search algorithm and the surrogate-model method for optimization…”
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