We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models.
Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters.
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The efficiency of Brain-Computer Interfaces (BCI) largely depends upon a reliable extraction of informative features from the high-dimensional EEG signal.
We propose a method called ideal regression for approximating an arbitrary system of polynomial equations by a system of a particular type.