Popularized by the long short-term memory (LSTM), multiplicative gates have become a standard means to design artificial neural networks with intentionally organized information flow. Notable examples of such architectures include gated recurrent units (GRU) and highway networks. In this work, we first focus on the evaluation of each of the classical gated architectures for language modeling fo...

Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) require 4 gates to learn shortand long-term dependencies for a given sequence of basic elements. Recently, “Gated Recurrent Unit” (GRU) has been introduced and requires fewer gates than LSTM (reset and update gates), to code shortand long-term dependencies and reaches equivalent performances to LSTM, with less processing time during ...

RNN is a powerful model for sequence data but suffers from gradient vanishing and explosion, thus difficult to be trained to capture long range dependences. But people have proposed LSTM and GRU, which try to model the differences between adjacent data frame rather than the data frame itself. By doing so, it allows the error to back propagate throw longer time without vanishing. Also instead of...

The Recurrent Neural Network (RNN) is an extremely powerful sequence model that is often difficult to train. The Long Short-Term Memory (LSTM) is a specific RNN architecture whose design makes it much easier to train. While wildly successful in practice, the LSTM’s architecture appears to be ad-hoc so it is not clear if it is optimal, and the significance of its individual components is unclear...

Recently recurrent neural networks (RNN) has been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN is a difficult task, partly because there are many competing and complex hidden units (such as LSTM and GRU). We propose a gated unit for RNN, named as Minimal Gated Unit (MGU), since it only contains one gate, which is a minimal design a...

Recurrent Neural Networks architectures excel at processing sequences by modelling dependencies over different timescales. The recently introduced Recurrent Weighted Average (RWA) unit captures long term dependencies far better than an LSTM on several challenging tasks. The RWA achieves this by applying attention to each input and computing a weighted average over the full history of its comput...

In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments...

Recurrent Neural Networks architectures excel at processing sequences by modelling dependencies over different timescales. The recently introduced Recurrent Weighted Average (RWA) unit captures long term dependencies far better than an LSTM on several challenging tasks. The RWA achieves this by applying attention to each input and computing a weighted average over the full history of its comput...

The difficulty in analyzing LSTM-like recurrent neural networks lies in the complex structure of the recurrent unit, which induces highly complex nonlinear dynamics. In this paper, we design a new simple recurrent unit, which we call Prototypical Recurrent Unit (PRU). We verify experimentally that PRU performs comparably to LSTM and GRU. This potentially enables PRU to be a prototypical example...

Deep learning, Recurrent Neural Networks (RNN) in particular have shown superior accuracy in a large variety of tasks including machine translation, language understanding, and movie frames generation. However, these deep learning approaches are very expensive in terms of computation. In most cases, Graphic Processing Units (GPUs) are in used for large scale implementations. Meanwhile, energy e...