Neural Information Processing Systems http://nips.cc/

This approach is however difficult to extend to the case in which one wants to compute an OT map (a Monge map) between the original measures.

However, models are rarely well-specified in practice.

Put plainly, the class of an image is invariant to the scale at which it is viewed.

Modern distributed training of machine learning models often suffers from high communication overhead for synchronizing stochastic gradients and model parameters.

Furthermore, we conduct a large-scale experiment to verify the correlation and training strategy.

Deep learning relies critically on good parameter initialization prior to training.

Experimental results demonstrate that our method achieves state-of-the-art performance on four well-established knowledge graph completion benchmarks.