At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical.
Ranked #1 on Multi-Armed Bandits on Mushroom
We show that this form of adversarial training converges to a degenerate global minimum, wherein small curvature artifacts near the data points obfuscate a linear approximation of the loss.
Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs), serve as a fundamental building block for many sequence learning tasks, including machine translation, language modeling, and question answering.
Ranked #9 on Language Modelling on WikiText-2
In this work, we study how depthwise separable convolutions can be applied to neural machine translation.
Ranked #26 on Machine Translation on WMT2014 English-German
We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents.
We describe a new training methodology for generative adversarial networks.
Ranked #3 on Image Generation on LSUN Cat 256 x 256