10.1k citations · 22.8k across the 27 of their papers we have counts for
5 papers · 1 filter
Towards Conceptual Compression
Karol Gregor, Frederic Besse, Danilo Jimenez Rezende +2
We introduce a simple recurrent variational auto-encoder architecture that significantly improves image modeling. The system represents the state-of-the-art in latent variable mode…
One-Shot Generalization in Deep Generative Models
Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka +2
Humans have an impressive ability to reason about new concepts and experiences from just a single example. In particular, humans have an ability for one-shot generalization: an abi…
Weight Uncertainty in Neural Networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu +1
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backp…
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra
We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scal…
Natural Evolution Strategies
Daan Wierstra, Tom Schaul, Tobias Glasmachers +2
This paper presents Natural Evolution Strategies (NES), a recent family of algorithms that constitute a more principled approach to black-box optimization than established evolutio…