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Found 5 papers, 0 papers with code
Delay-adaptive step-sizes for asynchronous learning
In this paper, we show that it is possible to use learning rates that depend on the actual time-varying delays in the system.
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees
We introduce novel convergence results for asynchronous iterations that appear in the analysis of parallel and distributed optimization algorithms.
Distributed learning with compressed gradients
Asynchronous computation and gradient compression have emerged as two key techniques for achieving scalability in distributed optimization for large-scale machine learning.
Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server
This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems.
An Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimization
Mini-batch optimization has proven to be a powerful paradigm for large-scale learning.
