14.5k citations · 48.8k across the 185 of their papers we have counts for
6 papers · 1 filter
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…
Bounding the Test Log-Likelihood of Generative Models
Yoshua Bengio, Li Yao, Kyunghyun Cho
Several interesting generative learning algorithms involve a complex probability distribution over many random variables, involving intractable normalization constants or latent va…
Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks
Caglar Gulcehre, Kyunghyun Cho, Razvan Pascanu +1
In this paper we propose and investigate a novel nonlinear unit, called unit, for deep neural networks. The proposed unit receives signals from several projections of a…
Classifying and Visualizing Motion Capture Sequences using Deep Neural Networks
Kyunghyun Cho, Xi Chen
The gesture recognition using motion capture data and depth sensors has recently drawn more attention in vision recognition. Currently most systems only classify dataset with a cou…
Understanding Dropout: Training Multi-Layer Perceptrons with Auxiliary Independent Stochastic Neurons
Kyunghyun Cho
In this paper, a simple, general method of adding auxiliary stochastic neurons to a multi-layer perceptron is proposed. It is shown that the proposed method is a generalization of…
Boltzmann Machines and Denoising Autoencoders for Image Denoising
Kyunghyun Cho
Image denoising based on a probabilistic model of local image patches has been employed by various researchers, and recently a deep (denoising) autoencoder has been proposed by Bur…