309 citations · 1.2k across the 9 of their papers we have counts for
9 papers
Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks
Lasse Espeholt, Shreya Agrawal, Casper Sønderby +7
The problem of forecasting weather has been scientifically studied for centuries due to its high impact on human lives, transportation, food production and energy management, among…
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression
Rianne van den Berg, Alexey A. Gritsenko, Mostafa Dehghani +2
In this paper we analyse and improve integer discrete flows for lossless compression. Integer discrete flows are a recently proposed class of models that learn invertible transform…
MetNet: A Neural Weather Model for Precipitation Forecasting
Casper Kaae Sønderby, Lasse Espeholt, Jonathan Heek +6
Weather forecasting is a long standing scientific challenge with direct social and economic impact. The task is suitable for deep neural networks due to vast amounts of continuousl…
Neural Machine Translation with Characters and Hierarchical Encoding
Alexander Rosenberg Johansen, Jonas Meinertz Hansen, Elias Khazen Obeid +2
Most existing Neural Machine Translation models use groups of characters or whole words as their unit of input and output. We propose a model with a hierarchical char2word encoder,…
Amortised MAP Inference for Image Super-resolution
Casper Kaae Sønderby, Jose Caballero, Lucas Theis +2
Image super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high-resolution images can explain the same downsampled image. Most current sin…
Auxiliary Deep Generative Models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby +1
Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative…
Ladder Variational Autoencoders
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe +2
Variational Autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limit…
Recurrent Spatial Transformer Networks
Søren Kaae Sønderby, Casper Kaae Sønderby, Lars Maaløe +1
We integrate the recently proposed spatial transformer network (SPN) [Jaderberg et. al 2015] into a recurrent neural network (RNN) to form an RNN-SPN model. We use the RNN-SPN to c…
Convolutional LSTM Networks for Subcellular Localization of Proteins
Søren Kaae Sønderby, Casper Kaae Sønderby, Henrik Nielsen +1
Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or feed-forward neural networks are often used although they have no natural…