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Found 7 papers, 0 papers with code
Distributed Newton Methods for Deep Neural Networks
First, to reduce the communication cost, we propose a diagonalization method such that an approximate Newton direction can be obtained without communication between machines.
A Sparse Nonlinear Classifier Design Using AUC Optimization
Batch learning methods for solving the kernelized version of this problem suffer from scalability and may not result in sparse classifiers.
A distributed block coordinate descent method for training $l_1$ regularized linear classifiers
In this paper we design a distributed algorithm for $l_1$ regularization that is much better suited for such systems than existing algorithms.
A Distributed Algorithm for Training Nonlinear Kernel Machines
This paper concerns the distributed training of nonlinear kernel machines on Map-Reduce.
An Empirical Evaluation of Sequence-Tagging Trainers
Here, we focus on discriminative models for sequence labeling.
An efficient distributed learning algorithm based on effective local functional approximations
In this paper we give a novel approach to the distributed training of linear classifiers (involving smooth losses and L2 regularization) that is designed to reduce the total communication costs.
