MUSIC GENERATION USING A BIDIRECTIONAL RECURRENT NEURAL NETWORK

Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network

Advanced driver assistance systems (ADAS) can be significantly improved with effective driver action prediction (DAP). Predicting driver actions early and accurately can help mitigate the effects of potentially unsafe driving behaviors and avoid possible accidents. In this paper, we formulate driver action prediction as a timeseries anomaly prediction problem. While the anomaly (driver actions ...

Video Description Using Bidirectional Recurrent Neural Networks

Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this mod...

Bidirectional recurrent neural networks

Music Generation Using Neural Networks

Sequence learning is attracting more and more attention both in industry and academic world with the wide usage of RNN and LSTM neural network architecture. Early this year, Google Brain team open sourced a research project named Magenta, which tries to provide a platform for musicians, artists and programmers to create their music and art works using machine intelligence. Several months later,...

Learning Distributed Word Representations For Bidirectional LSTM Recurrent Neural Network

Bidirectional long short-term memory (BLSTM) recurrent neural network (RNN) has been successfully applied in many tagging tasks. BLSTM-RNN relies on the distributed representation of words, which implies that the former can be futhermore improved through learning the latter better. In this work, we propose a novel approach to learn distributed word representations by training BLSTM-RNN on a spe...