Results 1 to 10 of about 140,150 (171)
Stochastic gradient descent for optimization for nuclear systems [PDF]
Scientific Reports, 2023 The use of gradient descent methods for optimizing k-eigenvalue nuclear systems has been shown to be useful in the past, but the use of k-eigenvalue gradients have proved computationally challenging due to their stochastic nature.
Austin Williams +5 more
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Semi-Stochastic Gradient Descent Methods [PDF]
Frontiers in Applied Mathematics and Statistics, 2017 In this paper we study the problem of minimizing the average of a large number of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descent), which runs for one or several epochs in each of which a single full gradient
Jakub Konečný, Peter Richtárik
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Byzantine Stochastic Gradient Descent
, 2018 This paper studies the problem of distributed stochastic optimization in an adversarial setting where, out of the $m$ machines which allegedly compute stochastic gradients every iteration, an $\alpha$-fraction are Byzantine, and can behave arbitrarily ...
Alistarh, Dan-Adrian +8 more
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Stochastic Gradient Descent for Kernel-Based Maximum Correntropy Criterion [PDF]
EntropyMaximum correntropy criterion (MCC) has been an important method in machine learning and signal processing communities since it was successfully applied in various non-Gaussian noise scenarios.
Tiankai Li +3 more
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Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System [PDF]
Sensors, 2020 The previous recommendation system applied the matrix factorization collaborative filtering (MFCF) technique to only single domains. Due to data sparsity, this approach has a limitation in overcoming the cold-start problem.
Nam D. Vo, Minsung Hong, Jason J. Jung
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Data-Dependent Stability of Stochastic Gradient Descent
, 2018 We establish a data-dependent notion of algorithmic stability for Stochastic Gradient Descent (SGD), and employ it to develop novel generalization bounds.
Kuzborskij, Ilja, Lampert, Christoph H.
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PAC–Bayes Guarantees for Data-Adaptive Pairwise Learning [PDF]
EntropyWe study the generalization properties of stochastic optimization methods under adaptive data sampling schemes, focusing on the setting of pairwise learning, which is central to tasks like ranking, metric learning, and AUC maximization.
Sijia Zhou, Yunwen Lei, Ata Kabán
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Stochastic gradient descent for wind farm optimization [PDF]
Wind Energy Science, 2023 It is important to optimize wind turbine positions to mitigate potential wake losses. To perform this optimization, atmospheric conditions, such as the inflow speed and direction, are assigned probability distributions according to measured data, which ...
J. Quick +4 more
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Communication-Censored Distributed Stochastic Gradient Descent [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 This paper develops a communication-efficient algorithm to solve the stochastic optimization problem defined over a distributed network, aiming at reducing the burdensome communication in applications such as distributed machine learning.Different from the existing works based on quantization and sparsification, we introduce a communication-censoring ...
Weiyu Li +4 more
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Byzantine-Resilient Decentralized Stochastic Gradient Descent [PDF]
IEEE Transactions on Circuits and Systems for Video Technology, 2022 Decentralized learning has gained great popularity to improve learning efficiency and preserve data privacy. Each computing node makes equal contribution to collaboratively learn a Deep Learning model. The elimination of centralized Parameter Servers (PS) can effectively address many issues such as privacy, performance bottleneck and single-point ...
Shangwei Guo +6 more
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