Search Results - "Optimization Methods in Machine Learning"

Gradient Optimization Methods in Machine Learning for the Identification of Dynamic Systems Parameters by Panteleev, A.V., Lobanov, A.V.

“…The article considers one of the possible ways to solve the problem of estimating the unknown parameters of dynamic models described by differential-algebraic…”
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Optimization methods in machine learning: Theory and applications by Saha, Ankan

“…We look at the integral role played by convex optimization in various machine learning problems. Over the last few years there has been a lot of machine…”
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Operator Theory for Analysis of Convex Optimization Methods in Machine Learning by Gallagher, Patrick W

“…As machine learning has more closely interacted with optimization, the concept of convexity has loomed large. Two properties beyond simple convexity have…”
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Secure Image Inference Using Pairwise Activation Functions by Jonas T. Agyepong, Mostafa I. Soliman, Yasutaka Wada, Keiji Kimura, Ahmed El-Mahdy

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A Survey of Optimization Methods From a Machine Learning Perspective by Sun, Shiliang, Cao, Zehui, Zhu, Han, Zhao, Jing

“… With the exponential growth of data amount and the increase of model complexity, optimization methods in machine learning face more and more…”
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Non-smooth Bayesian learning for artificial neural networks by Fakhfakh, Mohamed, Chaari, Lotfi, Bouaziz, Bassem, Gargouri, Faiez

“… A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively such as gradient-based method, Newton-type method, meta-heuristic method…”
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Stochastic normalized gradient descent with momentum for large-batch training by Zhao, Shen-Yi, Shi, Chang-Wei, Xie, Yin-Peng, Li, Wu-Jun

“…Stochastic gradient descent (SGD) and its variants have been the dominating optimization methods in machine learning…”
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Exploring Physics-Informed Neural Networks for the Generalized Nonlinear Sine-Gordon Equation by Deresse, Alemayehu Tamirie, Dufera, Tamirat Temesgen

“…The nonlinear sine-Gordon equation is a prevalent feature in numerous scientific and engineering problems. In this paper, we propose a machine learning-based…”
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Learning From Mistakes: A Multilevel Optimization Framework by Zhang, Li, Garg, Bhanu, Sridhara, Pradyumna, Hosseini, Ramtin, Xie, Pengtao

“…Bi-level optimization methods in machine learning are popularly effective in subdomains of neural architecture search, data reweighting, etc…”
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Non-smooth Bayesian learning for artificial neural networks: Non-smooth Bayesian learning by Fakhfakh, Mohamed, Chaari, Lotfi, Bouaziz, Bassem, Gargouri, Faiez

“… A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively such as gradient-based method, Newton-type method, meta-heuristic method…”
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Investigation of training performance of convolutional neural networks evolved by genetic algorithms using an activity function by Betere, Job Isaac, Kinjo, Hiroshi, Nakazono, Kunihiko, Oshiro, Naoki

“…) evolved by genetic algorithms (GA) using an activity function for image recognition. Globally, GA has been considered as one of the most robust search optimization methods in machine learning and artificial intelligent systems…”
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Stochastic Normalized Gradient Descent with Momentum for Large-Batch Training by Shen-Yi, Zhao, Chang-Wei, Shi, Yin-Peng, Xie, Wu-Jun, Li

“…Stochastic gradient descent~(SGD) and its variants have been the dominating optimization methods in machine learning…”
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Learning with Less: Low-Rank Dynamics, Communication, and Introspection in Machine Learning by Baker, Bradley

“…The enclosed research is a focused empirical and theoretical analysis of the optimization methods in machine learning, and the underlying role that the matrix rank of utilized learning statistics…”
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Topics in Machine Learning Optimization by Fang, Biyi

“…Recently, machine learning and deep learning, which have made many theoretical and empirical breakthroughs and is widely applied in various fields, attract a…”
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Large-scale learning with AdaGrad on Spark by Hadgu, Asmelash Teka, Nigam, Aastha, Diaz-Aviles, Ernesto

“… (and often non-convex) functions and one of the most popular stochastic optimization methods in machine learning today…”
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A Survey of Optimization Methods from a Machine Learning Perspective by Sun, Shiliang, Cao, Zehui, Zhu, Han, Zhao, Jing

“… With the exponential growth of data amount and the increase of model complexity, optimization methods in machine learning face more and more…”
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Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses by Dereich, Steffen, Jentzen, Arnulf, Riekert, Adrian

“… The default learning rate schedules for SGD optimization methods in machine learning implementation frameworks such as TensorFlow and Pytorch are constant learning rates…”
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Meta-Learning Parameterized First-Order Optimizers using Differentiable Convex Optimization by Gautam, Tanmay, Pfrommer, Samuel, Sojoudi, Somayeh

“…Conventional optimization methods in machine learning and controls rely heavily on first-order update rules…”
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Machines Explaining Linear Programs by Steinmann, David, Zečević, Matej, Devendra Singh Dhami, Kersting, Kristian

“… Although successful, these methods have mostly focused on the deep learning methods while the fundamental optimization methods in machine learning such as linear programs (LP) have been left…”
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Conformal Symplectic and Relativistic Optimization by França, Guilherme, Sulam, Jeremias, Robinson, Daniel P, Vidal, René

“…Arguably, the two most popular accelerated or momentum-based optimization methods in machine learning are Nesterov's accelerated gradient and Polyaks's heavy ball, both corresponding to different…”
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