This set embedding represents the "average" of the subreads and can be decoded into a prediction of the clean sequence.
Influence functions efficiently estimate the effect of removing a single training data point on a model's learned parameters.
Developing and deploying machine learning models safely depends on the ability to characterize and compare their abilities to generalize to new environments.
At longer time scales, we see slow growth of the entanglement, which may arise from dephasing mechanisms in the localized system or long-range interactions mediated by the central degree of freedom.
Disordered Systems and Neural Networks
In ablations on DBDC4 data from 2019, our semi-supervised learning methods improve the performance of a baseline BERT model by 2\% accuracy.
Previous work on neural noisy channel modeling relied on latent variable models that incrementally process the source and target sentence.
This paper describes Facebook FAIR's submission to the WMT19 shared news translation task.
Ranked #1 on Machine Translation on WMT2019 English-German
First, noting that in each image the embryo occupies a small subregion, we jointly train a region proposal network with the downstream classifier to isolate the embryo.
fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks.
Scheduling surgeries is a challenging task due to the fundamental uncertainty of the clinical environment, as well as the risks and costs associated with under- and over-booking.