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- Alex Graves, Greg Wayne, Ivo Danihelka
- ArXiv
- 2014

We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turingâ€¦ (More)

- Alex Graves, Greg Wayne, +17 authors Demis Hassabis
- Nature
- 2016

Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and toâ€¦ (More)

We present a unified framework for learning continuous control policies using backpropagation. It supports stochastic control by treating stochasticity in the Bellman equation as a deterministicâ€¦ (More)

- Ivo Danihelka, Greg Wayne, Benigno Uria, Nal Kalchbrenner, Alex Graves
- ICML
- 2016

We investigate a new method to augment recurrent neural networks with extra memory without increasing the number of network parameters. The system has an associative memory based on complex-valuedâ€¦ (More)

- Nicolas Heess, Dhruva TB, +9 authors David Silver
- ArXiv
- 2017

The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals. In practice, however, it is common to carefully hand-design theâ€¦ (More)

- Ann Kennedy, Greg Wayne, Patrick Kaifosh, Karina AlviÃ±a, L. F. Abbott, Nathaniel B. Sawtell
- Nature Neuroscience
- 2014

Mormyrid electric fish are a model system for understanding how neural circuits predict the sensory consequences of motor acts. Medium ganglion cells in the electrosensory lobe create negative imagesâ€¦ (More)

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew preciselyâ€¦ (More)

- Adam H. Marblestone, Greg Wayne, Konrad P. KÃ¶rding
- Front. Comput. Neurosci.
- 2016

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew preciselyâ€¦ (More)

- Mevlana Gemici, Chia-Chun Hung, +5 authors Timothy P. Lillicrap
- ArXiv
- 2017

We consider the general problem of modeling temporal data with long-range dependencies, wherein new observations are fully or partially predictable based on temporally-distant, past observations. Aâ€¦ (More)

- Josh Merel, Yuval Tassa, +5 authors Nicolas Heess
- ArXiv
- 2017

Rapid progress in deep reinforcement learning has made it increasingly feasible to train controllers for high-dimensional humanoid bodies. However, methods that use pure reinforcement learning withâ€¦ (More)