SFX by Ex Libris Inc.

Title:	Hybrid computing using a neural network with dynamic external memory
Source:	Nature [0028-0836] Graves, Alex yr:2016

Basic

Full text

Full text available via Nature

Year:

Volume:

Issue:

Start Page:

Document delivery

Request document via Library/Bibliothek

Users interested in this article also expressed an interest in the following:
description	1.	Mitchell, M. I. M. "Machine learning: Trends, perspectives, and prospects." Science 349.6245 (2015): 255-260.
description	2.	Mnih, V. "Human-level control through deep reinforcement learning." Nature 518.7540 (2015): 529-533.
description	3.	Frank, Anne G E J. "Human EEG Uncovers Latent Generalizable Rule Structure during Learning." The Journal of neuroscience 34.13 (2014): 4677-4685.
description	4.	Summerfield, C. "Oh, rats! Post-error behavioral adjustment in creatures great and small." Nature neuroscience 16.12 (2013): 1715-1716.
description	5.	Shashua, A. "Turing centenary : Is the brain a good model for machine intelligence?" Nature 482.7386 (2012): 462-463.
description	6.	Ghahramani, Z. "Probabilistic machine learning and artificial intelligence." Nature 521.7553 (2015): 452-9.
description	7.	Brynjolfsson, E. "What can machine learning do? Workforce implications." Science 358.6370 (2017): 1530-1534.
description	8.	LeCun, Y. "Deep learning." Nature 521.7553 (2015): 436-444.
description	9.	Silver, D. "Mastering the game of Go with deep neural networks and tree search." Nature 529.7587 (2016): 484-489.
description	10.	Grimmer, J. "We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together." PS, political science & politics 48.1 (2015): 80-83.
description	11.	Domingos, P. "A Few Useful Things to Know About Machine Learning." Communications of the ACM 55.10 (2012): 78-87.
description	12.	Daw, Nathaniel D. "Prioritized memory access explains planning and hippocampal replay." Nature Neuroscience 21.11 (2018): 1609-1617.
description	13.	Littman, Michael L. "Reinforcement learning improves behaviour from evaluative feedback." Nature 521.7553 (2015): 445-451.
description	14.	Hassabis, D. "Neuroscience-Inspired Artificial Intelligence." Neuron. 95.2: 245-258.
description	15.	Jiang, J. "Attention sharpens the distinction between expected and unexpected percepts in the visual brain." The Journal of neuroscience 33.47 (2013): 18438-18447.
description	16.	Johnson, Dominic D. P. H. "The evolution of overconfidence." Nature 477.7364 (2011): 317-320.
description	17.	Painter, David R. "Neural responses to target features outside a search array are enhanced during conjunction but not unique-feature search." The Journal of neuroscience 34.9 (2014): 3390-3401.
description	18.	Devaine, M. "Theory of Mind: Did Evolution Fool Us?" PLoS ONE 9.2 (2014).
description	19.	Grubert, A. "Spatial attention can be allocated rapidly and in parallel to new visual objects." Current biology 24.2 (2014): 193-198.
description	20.	Cavanagh, James F. "Frontal Theta Reflects Uncertainty and Unexpectedness during Exploration and Exploitation." Cerebral cortex 22.11 (2012): 2575-2586.