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Found 8 papers, 5 papers with code
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression
Furthermore, we zoom in on the effect of gradient bias due to the straight-through estimator in integer discrete flows, and demonstrate that its influence is highly dependent on architecture choices and less prominent than previously thought.
MetNet: A Neural Weather Model for Precipitation Forecasting
Weather forecasting is a long standing scientific challenge with direct social and economic impact.
Neural Machine Translation with Characters and Hierarchical Encoding
Most existing Neural Machine Translation models use groups of characters or whole words as their unit of input and output.
Amortised MAP Inference for Image Super-resolution
We show that, using this architecture, the amortised MAP inference problem reduces to minimising the cross-entropy between two distributions, similar to training generative models.
Auxiliary Deep Generative Models
The auxiliary variables leave the generative model unchanged but make the variational distribution more expressive.
Ranked #49 on
Image Classification
on SVHN
Ladder Variational Autoencoders
Variational Autoencoders are powerful models for unsupervised learning.
Recurrent Spatial Transformer Networks
We investigate different down-sampling factors (ratio of pixel in input and output) for the SPN and show that the RNN-SPN model is able to down-sample the input images without deteriorating performance.
Convolutional LSTM Networks for Subcellular Localization of Proteins
Machine learning is widely used to analyze biological sequence data.
