14.5k citations · 48.8k across the 185 of their papers we have counts for
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Semantic Noise Modeling for Better Representation Learning
Hyo-Eun Kim, Sangheum Hwang, Kyunghyun Cho
Latent representation learned from multi-layered neural networks via hierarchical feature abstraction enables recent success of deep learning. Under the deep learning framework, ge…
Fully Character-Level Neural Machine Translation without Explicit Segmentation
Jason Lee, Kyunghyun Cho, Thomas Hofmann
Most existing machine translation systems operate at the level of words, relying on explicit segmentation to extract tokens. We introduce a neural machine translation (NMT) model t…
Learning to Translate in Real-time with Neural Machine Translation
Jiatao Gu, Graham Neubig, Kyunghyun Cho +1
Translating in real-time, a.k.a. simultaneous translation, outputs translation words before the input sentence ends, which is a challenging problem for conventional machine transla…
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…
Context-Dependent Word Representation for Neural Machine Translation
Heeyoul Choi, Kyunghyun Cho, Yoshua Bengio
We first observe a potential weakness of continuous vector representations of symbols in neural machine translation. That is, the continuous vector representation, or a word embedd…
Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes
Caglar Gulcehre, Sarath Chandar, Kyunghyun Cho +1
We extend neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing a trainable memory addressing scheme. This addressing scheme maintains for e…