A citation-based method for searching scientific literature

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis. Nature 2015
Times Cited: 959







List of co-cited articles
154 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Nature 2015
20

Mastering the game of Go with deep neural networks and tree search.
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot,[...]. Nature 2016
19

Mastering the game of Go without human knowledge.
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton,[...]. Nature 2017
521
19

Long short-term memory.
S Hochreiter, J Schmidhuber. Neural Comput 1997
9

A neural substrate of prediction and reward.
W Schultz, P Dayan, P R Montague. Science 1997
7

Prefrontal cortex as a meta-reinforcement learning system.
Jane X Wang, Zeb Kurth-Nelson, Dharshan Kumaran, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Demis Hassabis, Matthew Botvinick. Nat Neurosci 2018
129
6

Building machines that learn and think like people.
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, Samuel J Gershman. Behav Brain Sci 2017
200
6

Dermatologist-level classification of skin cancer with deep neural networks.
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, Sebastian Thrun. Nature 2017
5

Reinforcement Learning, Fast and Slow.
Matthew Botvinick, Sam Ritter, Jane X Wang, Zeb Kurth-Nelson, Charles Blundell, Demis Hassabis. Trends Cogn Sci 2019
74
6

Deep learning in neural networks: an overview.
Jürgen Schmidhuber. Neural Netw 2015
4

Molecular de-novo design through deep reinforcement learning.
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, Hongming Chen. J Cheminform 2017
253
4

Representation learning: a review and new perspectives.
Yoshua Bengio, Aaron Courville, Pascal Vincent. IEEE Trans Pattern Anal Mach Intell 2013
957
4

Grandmaster level in StarCraft II using multi-agent reinforcement learning.
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev,[...]. Nature 2019
95
4



A distributional code for value in dopamine-based reinforcement learning.
Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, Matthew Botvinick. Nature 2020
70
5

Reinforcement learning in multidimensional environments relies on attention mechanisms.
Yael Niv, Reka Daniel, Andra Geana, Samuel J Gershman, Yuan Chang Leong, Angela Radulescu, Robert C Wilson. J Neurosci 2015
138
4

Performance-optimized hierarchical models predict neural responses in higher visual cortex.
Daniel L K Yamins, Ha Hong, Charles F Cadieu, Ethan A Solomon, Darren Seibert, James J DiCarlo. Proc Natl Acad Sci U S A 2014
530
4

Deep supervised, but not unsupervised, models may explain IT cortical representation.
Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte. PLoS Comput Biol 2014
396
4



The rise of deep learning in drug discovery.
Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, Thomas Blaschke. Drug Discov Today 2018
421
3

Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness.
Michiru Nishita, Seung-Yeol Park, Tadashi Nishio, Koki Kamizaki, ZhiChao Wang, Kota Tamada, Toru Takumi, Ryuju Hashimoto, Hiroki Otani, Gregory J Pazour,[...]. Sci Rep 2017
3

Machine learning for molecular and materials science.
Keith T Butler, Daniel W Davies, Hugh Cartwright, Olexandr Isayev, Aron Walsh. Nature 2018
531
3

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel,[...]. Science 2018
171
3

Planning chemical syntheses with deep neural networks and symbolic AI.
Marwin H S Segler, Mike Preuss, Mark P Waller. Nature 2018
414
3


Mastering Atari, Go, chess and shogi by planning with a learned model.
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel,[...]. Nature 2020
38
7

Learning task-state representations.
Yael Niv. Nat Neurosci 2019
67
4

Deep Reinforcement Learning and Its Neuroscientific Implications.
Matthew Botvinick, Jane X Wang, Will Dabney, Kevin J Miller, Zeb Kurth-Nelson. Neuron 2020
26
11


Replay Comes of Age.
David J Foster. Annu Rev Neurosci 2017
116
3

Cognitive maps in rats and men.
E C TOLMAN. Psychol Rev 1948
3


Dynamic Interaction between Reinforcement Learning and Attention in Multidimensional Environments.
Yuan Chang Leong, Angela Radulescu, Reka Daniel, Vivian DeWoskin, Yael Niv. Neuron 2017
107
3

Inferring relevance in a changing world.
Robert C Wilson, Yael Niv. Front Hum Neurosci 2012
75
4

Learning the value of information in an uncertain world.
Timothy E J Behrens, Mark W Woolrich, Mark E Walton, Matthew F S Rushworth. Nat Neurosci 2007
984
3

Neuroscience-Inspired Artificial Intelligence.
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, Matthew Botvinick. Neuron 2017
218
3

Opportunities and obstacles for deep learning in biology and medicine.
Travers Ching, Daniel S Himmelstein, Brett K Beaulieu-Jones, Alexandr A Kalinin, Brian T Do, Gregory P Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M Hoffman,[...]. J R Soc Interface 2018
525
3

Applications of Deep Learning and Reinforcement Learning to Biological Data.
Mufti Mahmud, Mohammed Shamim Kaiser, Amir Hussain, Stefano Vassanelli. IEEE Trans Neural Netw Learn Syst 2018
71
4

Highly accurate protein structure prediction with AlphaFold.
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko,[...]. Nature 2021
3

Active Inference: A Process Theory.
Karl Friston, Thomas FitzGerald, Francesco Rigoli, Philipp Schwartenbeck, Giovanni Pezzulo. Neural Comput 2017
253
3


Prediction of drug-target interaction networks from the integration of chemical and genomic spaces.
Yoshihiro Yamanishi, Michihiro Araki, Alex Gutteridge, Wataru Honda, Minoru Kanehisa. Bioinformatics 2008
473
2


Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.
Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang, Jinbo Xu. PLoS Comput Biol 2017
403
2

Deep learning for computational biology.
Christof Angermueller, Tanel Pärnamaa, Leopold Parts, Oliver Stegle. Mol Syst Biol 2016
483
2

In situ click chemistry generation of cyclooxygenase-2 inhibitors.
Atul Bhardwaj, Jatinder Kaur, Melinda Wuest, Frank Wuest. Nat Commun 2017
2

Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network.
Hu Chen, Yi Zhang, Mannudeep K Kalra, Feng Lin, Yang Chen, Peixi Liao, Jiliu Zhou, Ge Wang. IEEE Trans Med Imaging 2017
274
2



Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.