Specifically, we target semi-supervised classification performance, and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations useful for this task.
Transformer networks have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling.
SOTA for Language Modelling on Hutter Prize
We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements.
#2 best model for Machine Translation on WMT2014 English-German
Adaptive optimization methods such as AdaGrad, RMSProp and Adam have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates.
Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output.
Our observations are consistent for multiple network architectures, datasets, and tasks, which imply that: 1) training a large, over-parameterized model is often not necessary to obtain an efficient final model, 2) learned "important" weights of the large model are typically not useful for the small pruned model, 3) the pruned architecture itself, rather than a set of inherited "important" weights, is more crucial to the efficiency in the final model, which suggests that in some cases pruning can be useful as an architecture search paradigm.
This is a strict hierarchy: when a larger constituent ends, all of the smaller constituents that are nested within it must also be closed.
Work on the problem of contextualized word representation---the development of reusable neural network components for sentence understanding---has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo.
For the challenging semantic image segmentation task the most efficient models have traditionally combined the structured modelling capabilities of Conditional Random Fields (CRFs) with the feature extraction power of CNNs.