75.5k citations · 86.4k across the 10 of their papers we have counts for
10 papers
Video Pixel Networks
Nal Kalchbrenner, Aaron van den Oord, Karen Simonyan +4
We propose a probabilistic video model, the Video Pixel Network (VPN), that estimates the discrete joint distribution of the raw pixel values in a video. The model and the neural a…
Neural Machine Translation in Linear Time
Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan +3
We present a novel neural network for processing sequences. The ByteNet is a one-dimensional convolutional neural network that is composed of two parts, one to encode the source se…
WaveNet: A Generative Model for Raw Audio
Aaron van den Oord, Sander Dieleman, Heiga Zen +6
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for e…
Reading Text in the Wild with Convolutional Neural Networks
Max Jaderberg, Karen Simonyan, Andrea Vedaldi +1
In this work we present an end-to-end system for text spotting -- localising and recognising text in natural scene images -- and text based image retrieval. This system is based on…
Deep Structured Output Learning for Unconstrained Text Recognition
Max Jaderberg, Karen Simonyan, Andrea Vedaldi +1
We develop a representation suitable for the unconstrained recognition of words in natural images: the general case of no fixed lexicon and unknown length. To this end we propose a…
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan, Andrew Zisserman
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluati…