5k citations · 18.1k across the 28 of their papers we have counts for
29 papers
Bayesian Flow Networks
Alex Graves, Rupesh Kumar Srivastava, Timothy Atkinson +1
This paper introduces Bayesian Flow Networks (BFNs), a new class of generative model in which the parameters of a set of independent distributions are modified with Bayesian infere…
A Practical Sparse Approximation for Real Time Recurrent Learning
Jacob Menick, Erich Elsen, Utku Evci +3
Current methods for training recurrent neural networks are based on backpropagation through time, which requires storing a complete history of network states, and prohibits updatin…
Associative Compression Networks for Representation Learning
Alex Graves, Jacob Menick, Aaron van den Oord
This paper introduces Associative Compression Networks (ACNs), a new framework for variational autoencoding with neural networks. The system differs from existing variational autoe…
The Kanerva Machine: A Generative Distributed Memory
Yan Wu, Greg Wayne, Alex Graves +1
We present an end-to-end trained memory system that quickly adapts to new data and generates samples like them. Inspired by Kanerva's sparse distributed memory, it has a robust dis…
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
Aaron van den Oord, Yazhe Li, Igor Babuschkin +19
The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages…
Noisy Networks for Exploration
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot +9
We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to…