14.5k citations · 48.8k across the 185 of their papers we have counts for
14 papers · 1 filter
Larger-Context Language Modelling
Tian Wang, Kyunghyun Cho
In this work, we propose a novel method to incorporate corpus-level discourse information into language modelling. We call this larger-context language model. We introduce a late f…
First Step toward Model-Free, Anonymous Object Tracking with Recurrent Neural Networks
Quan Gan, Qipeng Guo, Zheng Zhang +1
In this paper, we propose and study a novel visual object tracking approach based on convolutional networks and recurrent networks. The proposed approach is distinct from the exist…
Natural Language Understanding with Distributed Representation
Kyunghyun Cho
This is a lecture note for the course DS-GA 3001 <Natural Language Understanding with Distributed Representation> at the Center for Data Science , New York University in Fall, 2015…
ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
Francesco Visin, Marco Ciccone, Adriana Romero +5
We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks…
A Controller-Recognizer Framework: How necessary is recognition for control?
Marcin Moczulski, Kelvin Xu, Aaron Courville +1
Recently there has been growing interest in building active visual object recognizers, as opposed to the usual passive recognizers which classifies a given static image into a pred…
Oracle performance for visual captioning
Li Yao, Nicolas Ballas, Kyunghyun Cho +2
The task of associating images and videos with a natural language description has attracted a great amount of attention recently. Rapid progress has been made in terms of both deve…
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…
Attention-Based Models for Speech Recognition
Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk +2
Recurrent sequence generators conditioned on input data through an attention mechanism have recently shown very good performance on a range of tasks in- cluding machine translation…
ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
Francesco Visin, Kyle Kastner, Kyunghyun Cho +3
In this paper, we propose a deep neural network architecture for object recognition based on recurrent neural networks. The proposed network, called ReNet, replaces the ubiquitous…
Learning to Understand Phrases by Embedding the Dictionary
Felix Hill, Kyunghyun Cho, Anna Korhonen +1
Distributional models that learn rich semantic word representations are a success story of recent NLP research. However, developing models that learn useful representations of phra…
On Using Monolingual Corpora in Neural Machine Translation
Caglar Gulcehre, Orhan Firat, Kelvin Xu +6
Recent work on end-to-end neural network-based architectures for machine translation has shown promising results for En-Fr and En-De translation. Arguably, one of the major factors…
Describing Videos by Exploiting Temporal Structure
Li Yao, Atousa Torabi, Kyunghyun Cho +4
Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are…
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu, Jimmy Ba, Ryan Kiros +5
Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe…
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…