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Found 6 papers, 1 papers with code
AdaScale SGD: A User-Friendly Algorithm for Distributed Training
When using large-batch training to speed up stochastic gradient descent, learning rates must adapt to new batch sizes in order to maximize speed-ups and preserve model quality.
AdaScale SGD: A Scale-Invariant Algorithm for Distributed Training
When using distributed training to speed up stochastic gradient descent, learning rates must adapt to new scales in order to maintain training effectiveness.
Training Deep Models Faster with Robust, Approximate Importance Sampling
In theory, importance sampling speeds up stochastic gradient algorithms for supervised learning by prioritizing training examples.
A Fast, Principled Working Set Algorithm for Exploiting Piecewise Linear Structure in Convex Problems
By reducing optimization to a sequence of smaller subproblems, working set algorithms achieve fast convergence times for many machine learning problems.
StingyCD: Safely Avoiding Wasteful Updates in Coordinate Descent
Coordinate descent (CD) is a scalable and simple algorithm for solving many optimization problems in machine learning.
Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization
We develop methods for rapidly identifying important components of a convex optimization problem for the purpose of achieving fast convergence times.
