Results 51 to 60 of about 1,273 (111)

Analysis of stochastic gradient descent in continuous time [PDF]

Statistics and Computing, 2021
AbstractStochastic gradient descent is an optimisation method that combines classical gradient descent with random subsampling within the target functional. In this work, we introduce the stochastic gradient process as a continuous-time representation of stochastic gradient descent.
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Balancing Privacy and Utility in Artificial Intelligence-Based Clinical Decision Support: Empirical Evaluation Using De-Identified Electronic Health Record Data

Applied Sciences
The secondary use of electronic health records is essential for developing artificial intelligence-based clinical decision support systems. However, even after direct identifiers are removed, de-identified electronic health records remain vulnerable to ...
Jungwoo Lee, Kyu Hee Lee
doaj   +1 more source

On Scalable Inference with Stochastic Gradient Descent

CoRR, 2017
In many applications involving large dataset or online updating, stochastic gradient descent (SGD) provides a scalable way to compute parameter estimates and has gained increasing popularity due to its numerical convenience and memory efficiency. While the asymptotic properties of SGD-based estimators have been established decades ago, statistical ...
Yixin Fang, Jinfeng Xu, Lei Yang
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Attentional-Biased Stochastic Gradient Descent

Trans. Mach. Learn. Res., 2020
In this paper, we present a simple yet effective provable method (named ABSGD) for addressing the data imbalance or label noise problem in deep learning. Our method is a simple modification to momentum SGD where we assign an individual importance weight to each sample in the mini-batch.
Qi Qi 0006, Yi Xu 0008, Wotao Yin, Rong Jin 0001, Tianbao Yang   +4 more
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Revisiting Stochastic Approximation and Stochastic Gradient Descent

CoRR
31 ...
Rajeeva Laxman Karandikar, Bhamidi Visweswara Rao, Mathukumalli Vidyasagar   +2 more
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On the regularizing property of stochastic gradient descent

Inverse Problems, 2018
Stochastic gradient descent is one of the most successful approaches for solving large-scale problems, especially in machine learning and statistics. At each iteration, it employs an unbiased estimator of the full gradient computed from one single randomly selected data point.
Bangti Jin, Xiliang Lu
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Federated Accelerated Stochastic Gradient Descent

CoRR, 2020
Accepted to NeurIPS 2020. Best paper in International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020 (FL-ICML'20).
Honglin Yuan, Tengyu Ma 0001
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Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System

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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A Static Security Region Analysis of New Power Systems Based on Improved Stochastic–Batch Gradient Pile Descent

Applied Sciences
The uncertainty in the new power system has increased, leading to limitations in traditional stability analysis methods. Therefore, considering the perspective of the three-dimensional static security region (SSR), we propose a novel approach for system ...
Jiahui Wu, Yide Zhou, Haiyun Wang, Weiqing Wang   +3 more
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Unforgeability in Stochastic Gradient Descent

Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, 2023
Teodora Baluta, Ivica Nikolic, Racchit Jain, Divesh Aggarwal, Prateek Saxena   +4 more
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