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Found 5 papers, 1 papers with code
On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition
While this wealth of information can be challenging to analyze using traditional methods, ML provides a seamless approach to this task.
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression
To reveal the true advantages of RR in the distributed learning with compression, we propose a new method called DIANA-RR that reduces the compression variance and has provably better convergence rates than existing counterparts with with-replacement sampling of stochastic gradients.
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation
We propose and study a new class of gradient communication mechanisms for communication-efficient training -- three point compressors (3PC) -- as well as efficient distributed nonconvex optimization algorithms that can take advantage of them.
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback
First proposed by Seide (2014) as a heuristic, error feedback (EF) is a very popular mechanism for enforcing convergence of distributed gradient-based optimization methods enhanced with communication compression strategies based on the application of contractive compression operators.
EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback
However, all existing analyses either i) apply to the single node setting only, ii) rely on very strong and often unreasonable assumptions, such global boundedness of the gradients, or iterate-dependent assumptions that cannot be checked a-priori and may not hold in practice, or iii) circumvent these issues via the introduction of additional unbiased compressors, which increase the communication cost.
