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Human-level control through deep reinforcement learning
This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Overcoming catastrophic forgetting in neural networks
- J. Kirkpatrick, Razvan Pascanu, R. Hadsell
- Computer ScienceProceedings of the National Academy of Sciences
- 2 December 2016
It is shown that it is possible to overcome the limitation of connectionist models and train networks that can maintain expertise on tasks that they have not experienced for a long time and selectively slowing down learning on the weights important for previous tasks.
Mastering the game of Go with deep neural networks and tree search
Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
Highly accurate protein structure prediction with AlphaFold
This work validated an entirely redesigned version of the neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experiment in a majority of cases and greatly outperforming other methods.
Mastering the game of Go without human knowledge
An algorithm based solely on reinforcement learning is introduced, without human data, guidance or domain knowledge beyond game rules, that achieves superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.
Hybrid computing using a neural network with dynamic external memory
A machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer.
Patients with hippocampal amnesia cannot imagine new experiences
- D. Hassabis, D. Kumaran, S. Vann, E. Maguire
- Psychology, BiologyProceedings of the National Academy of Sciences
- 30 January 2007
It is revealed that patients with primary damage to the hippocampus bilaterally could construct new imagined experiences in response to short verbal cues that outlined a range of simple commonplace scenarios, but were markedly impaired relative to matched control subjects at imagining new experiences.
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
This paper generalizes the AlphaZero approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games, and convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go.
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
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 than any previous…
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
This paper generalises the approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains, and convincingly defeated a world-champion program in each case.