Everyone wants to write beautiful and correct text, yet the lack of language skills, experience, or hasty typing can result in errors.
Our approach is also able to restore diacritics in words not seen during training with > 76% accuracy.
This work aims to research an automatic method for detecting Age-related Macular Degeneration (AMD) lesions in RGB eye fundus images.
In this work, we train the first monolingual Lithuanian transformer model on a relatively large corpus of Lithuanian news articles and compare various output decoding algorithms for abstractive news summarization.
The second level of optimization also makes the (ii) part remain constant irrespective of large $k$, as long as the dimension of the output is low.
They can be added to any convolutional layers, easily end-to-end trained, introduce minimal additional complexity, and let CNNs retain most of their benefits to the extent that they are needed.
A recent introduction of Transformer deep learning architecture made breakthroughs in various natural language processing tasks.
Thus in many situations $k$-fold cross-validation of ESNs can be done for virtually the same time complexity as a simple single split validation.