14.5k citations · 48.8k across the 185 of their papers we have counts for
8 papers · 1 filter
Convolutional Recurrent Neural Networks for Music Classification
Keunwoo Choi, George Fazekas, Mark Sandler +1
We introduce a convolutional recurrent neural network (CRNN) for music tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local feature extraction and recurr…
Describing Multimedia Content using Attention-based Encoder--Decoder Networks
Kyunghyun Cho, Aaron Courville, Yoshua Bengio
Whereas deep neural networks were first mostly used for classification tasks, they are rapidly expanding in the realm of structured output problems, where the observed target is co…
Gated Feedback Recurrent Neural Networks
Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho +1
In this work, we propose a novel recurrent neural network (RNN) architecture. The proposed RNN, gated-feedback RNN (GF-RNN), extends the existing approach of stacking multiple recu…
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho +1
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,…
End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results
Jan Chorowski, Dzmitry Bahdanau, Kyunghyun Cho +1
We replace the Hidden Markov Model (HMM) which is traditionally used in in continuous speech recognition with a bi-directional recurrent neural network encoder coupled to a recurre…
How to Construct Deep Recurrent Neural Networks
Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho +1
In this paper, we explore different ways to extend a recurrent neural network (RNN) to a \textit{deep} RNN. We start by arguing that the concept of depth in an RNN is not as clear…