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On the almost sure convergence of stochastic gradient descent in non-convex problems
In this paper, we analyze the trajectories of stochastic gradient descent (SGD) with the aim of
understanding their convergence properties in non-convex problems. We first show that the …
understanding their convergence properties in non-convex problems. We first show that the …
Distributed stochastic gradient descent: Nonconvexity, nonsmoothness, and convergence to local minima
Gradient-descent (GD) based algorithms are an indispensable tool for optimizing modern
machine learning models. The paper considers distributed stochastic GD (D-SGD)--a …
machine learning models. The paper considers distributed stochastic GD (D-SGD)--a …
Cb-dsl: Communication-efficient and byzantine-robust distributed swarm learning on non-iid data
The valuable data collected by IoT devices together with the resurgence of machine learning
(ML) stimulate the latest trend of artificial intelligence (AI) at the edge. However, traditional …
(ML) stimulate the latest trend of artificial intelligence (AI) at the edge. However, traditional …
Almost sure convergence rates analysis and saddle avoidance of stochastic gradient methods
The vast majority of convergence rates analysis for stochastic gradient methods in the
literature focus on convergence in expectation, whereas trajectory-wise almost sure …
literature focus on convergence in expectation, whereas trajectory-wise almost sure …
Online bootstrap inference with nonconvex stochastic gradient descent estimator
Computable access control: Embedding access control rules into euclidean space
L Dong, T Wu, W Jia, B Jiang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Access control is one of the most basic techniques to ensure the security of the information
system. The traditional access controls of information systems are usually performed based …
system. The traditional access controls of information systems are usually performed based …
3DPG: Distributed deep deterministic policy gradient algorithms for networked multi-agent systems
We present Distributed Deep Deterministic Policy Gradient (3DPG), a multi-agent actor-critic
(MAAC) algorithm for Markov games. Unlike previous MAAC algorithms, 3DPG is fully …
(MAAC) algorithm for Markov games. Unlike previous MAAC algorithms, 3DPG is fully …