Transformer architecture has taken the world of NLP and Deep Learning by storm since it was introduced in the paper "Attention Is All You Need", in 2017. Before Transformers, best performing NLP ...

Rotary Positional Embeddings (RoPE) is an advanced approach in artificial intelligence that enhances positional encoding in transformer models, especially for sequential data like language.

Reading morphologically complex words requires analysis of their morphemic subunits (e.g., play + er); however, the positional constraints of morphemic processing are still little understood. The ...

Deep-learning-based medical image segmentation techniques can assist doctors in disease diagnosis and rapid treatment. However, existing medical image segmentation models do not fully consider the ...

Application for training an autoencoder for generating an encoder that can be used as feature extractor for dimensionality and noise reduction, while the decoder can be used for synthetic data ...

Abstract: Transformer architecture has enabled recent progress in speech enhancement. Since Transformers are position-agostic, positional encoding is the de facto standard component used to enable ...