The prediction of chaotic time series with neural networks is a traditional practical problem of dynamic systems. This paper is not intended for proposing a new model or a new methodology, but to study carefully and thoroughly several aspects of a model on which there are no enough communicated experimental data, as well as to derive conclusions that would be of interest. The recurrent neural n...

We explore how deep recurrent neural network (RNN) architectures can be used to capture the structure within a genetic sequence. We first confirm that a characterlevel RNN can capture the non-random parts of DNA by comparing the perplexity obtained after training on a real genome to that obtained after training on a random sequence of nucleotides. We then train a bidirectional character-level R...

In this work, we propose a training algorithm for an audiovisual automatic speech recognition (AV-ASR) system using deep recurrent neural network (RNN).First, we train a deep RNN acoustic model with a Connectionist Temporal Classification (CTC) objective function. The frame labels obtained from the acoustic model are then used to perform a non-linear dimensionality reduction of the visual featu...

This paper describes the development of a deep learning methodology for semantic utterance classification (SUC) for use in domainspecific dialogue systems. Semantic classifiers need to account for a variety of instances where the utterance for the semantic domain class varies. In order to capture the candidate relationships between the semantic class and the word sequence in an utterance, we ha...

While vehicle license plate recognition (VLPR) is usually done with a sliding window approach, it can have limited performance on datasets with characters that are of variable width. This can be solved by hand-crafting algorithms to prescale the characters. While this approach can work fairly well, the recognizer is only aware of the pixels within each detector window, and fails to account for ...

Recurrent neural networks (RNNs) with jump ahead connections have been used in the computer vision tasks. Still, they have not been investigated well for automatic speech recognition (ASR) tasks. In other words, unfolded RNN has been shown to be an effective model for acoustic modeling tasks. This paper investigates how to elaborate a sophisticated unfolded deep RNN architecture in which recurr...

Objective We explored whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would improve model performance in predicting initial diagnosis of heart failure (HF) compared to conventional methods that ignore temporality. Materials and Methods Data were from a health system's EHR on 3884 incident HF cases and 28 903 controls, identified as pri...

We present a neural network based automatic post-editing (APE) system to improve raw machine translation (MT) output. Our neural model of APE (NNAPE) is based on a bidirectional recurrent neural network (RNN) model and consists of an encoder that encodes an MT output into a fixed-length vector from which a decoder provides a post-edited (PE) translation. APE translations produced by NNAPE show ...

Many neural networks exhibit stability in their activation patterns over time in response to inputs from sensors operating under real-world conditions. By capitalizing on this property of natural signals, we propose a Recurrent Neural Network (RNN) architecture called a delta network in which each neuron transmits its value only when the change in its activation exceeds a threshold. The executi...

A new modular recurrent neural network (MRNN)-based method for continuous Mandarin speech recognition (CMSR) is proposed. The MRNN recognizer is composed of four main modules. The first is a sub-MRNN module whose function is to generate discriminant functions for all 412 base-syllables. It accomplishes the task by using four recurrent neural network (RNN) submodules. The second is an RNN module...