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Found 4 papers, 1 papers with code
Primal-Dual Sequential Subspace Optimization for Saddle-point Problems
We introduce a new sequential subspace optimization method for large-scale saddle-point problems.
Asynchronous SGD without gradient delay for efficient distributed training
This is the main differentiator between our approach and other solutions, such as Elastic Asynchronous SGD or Downpour SGD, in which that maximal discrepancy is bounded by an expression that depends on the number of workers, due to gradient delay problem.
Low-bit Quantization of Neural Networks for Efficient Inference
Recent machine learning methods use increasingly large deep neural networks to achieve state of the art results in various tasks.
Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approach
We present a novel framework for applying deep neural networks (DNN) to soft decoding of linear codes at arbitrary block lengths.
