Abstract “Recurrent neural networks”(RNNs) are powerful artificial intelligence models that
have shown remarkable effectiveness in several tasks such as music generation, speech …

The self-attention mechanism is rapidly emerging as one of the most important key primitives
in neural networks (NNs) for its ability to identify the relations within input entities. The self …

Memory-augmented neural networks (MANNs)--which augment a traditional Deep Neural
Network (DNN) with an external, differentiable memory--are emerging as a promising …

Memory-Augmented Neural Networks (MANNs) have been shown to outperform Recurrent
Neural Networks (RNNs) in terms of long-term dependencies. Since MANNs are equipped …

Memory-augmented neural networks (MANNs) were introduced to handle long-term
dependent data efficiently. MANNs have shown promising results in question answering …

Various deep learning applications on smartphones have been rapidly rising, but training
deep neural networks (DNNs) has too large computational burden to be executed on a …

Приводится обзор реализаций нейронных сетей на программируемых логических
интегральных схемах (ПЛИС) типа FPGA (Field Programmable Gate Array) и на …

In this work, to limit the number of required attention inference hops in memory-augmented
neural networks, we propose an online adaptive approach called-memory-augmented …

The article provides a survey about the implementation of neural networks on
Programmable Logic Device (PLDs) such as FPGA (Field Programmable Gate Array) and …

Due to a combination of algorithmic advances, wide-spread availability of rich data sets, and
tremendous growth in compute availability, Deep Neural Networks (DNNs) have seen …