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NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform parallel model training.
Scalable Belief Propagation via Relaxed Scheduling
The ability to leverage large-scale hardware parallelism has been one of the key enablers of the accelerated recent progress in machine learning.
Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed on clusters to perform model fitting in parallel.
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