Open Access Helper

OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Hybrid computing using a neural network with dynamic external memory
Alex Graves, Greg Wayne, Malcolm Reynolds, et al.
Nature (2016) Vol. 538, Iss. 7626, pp. 471-476
Closed Access | Times Cited: 1358

Showing 1-25 of 1358 citing articles:

A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
Yong Yu, Xiaosheng Si, Changhua Hu, et al.
Neural Computation (2019) Vol. 31, Iss. 7, pp. 1235-1270
Closed Access | Times Cited: 3629
Continual lifelong learning with neural networks: A review
German I. Parisi, Ronald Kemker, Jose L. Part, et al.
Neural Networks (2019) Vol. 113, pp. 54-71
Open Access | Times Cited: 2461
Deep learning with coherent nanophotonic circuits
Yichen Shen, Nicholas C. Harris, Scott A. Skirlo, et al.
Nature Photonics (2017) Vol. 11, Iss. 7, pp. 441-446
Open Access | Times Cited: 2436
Relational inductive biases, deep learning, and graph networks
Peter Battaglia, Jessica B. Hamrick, Victor Bapst, et al.
arXiv (Cornell University) (2018)
Open Access | Times Cited: 2286
Building machines that learn and think like people
Brenden M. Lake, Tomer Ullman, Joshua B. Tenenbaum, et al.
Behavioral and Brain Sciences (2016) Vol. 40
Open Access | Times Cited: 2209
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, et al.
(2017), pp. 1988-1997
Open Access | Times Cited: 1721
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 1539
Deep Learning in Mobile and Wireless Networking: A Survey
Chaoyun Zhang, Paul Patras, Hamed Haddadi
IEEE Communications Surveys & Tutorials (2019) Vol. 21, Iss. 3, pp. 2224-2287
Open Access | Times Cited: 1498
Memory devices and applications for in-memory computing
Abu Sebastian, Manuel Le Gallo, Riduan Khaddam-Aljameh, et al.
Nature Nanotechnology (2020) Vol. 15, Iss. 7, pp. 529-544
Open Access | Times Cited: 1476
The rise of deep learning in drug discovery
Hongming Chen, Ola Engkvist, Yinhai Wang, et al.
Drug Discovery Today (2018) Vol. 23, Iss. 6, pp. 1241-1250
Open Access | Times Cited: 1462
Neuroscience-Inspired Artificial Intelligence
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, et al.
Neuron (2017) Vol. 95, Iss. 2, pp. 245-258
Open Access | Times Cited: 1334
11 TOPS photonic convolutional accelerator for optical neural networks
Xingyuan Xu, Mengxi Tan, Bill Corcoran, et al.
Nature (2021) Vol. 589, Iss. 7840, pp. 44-51
Open Access | Times Cited: 1140
Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 1133
A clinically applicable approach to continuous prediction of future acute kidney injury
Nenad Tomašev, Xavier Glorot, Jack W. Rae, et al.
Nature (2019) Vol. 572, Iss. 7767, pp. 116-119
Open Access | Times Cited: 850
Learning Combinatorial Optimization Algorithms over Graphs
Hanjun Dai, Elias B. Khalil, Yuyu Zhang, et al.
arXiv (Cornell University) (2017)
Open Access | Times Cited: 778
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Xin Yang, Yifei Wang, Ryan Byrne, et al.
Chemical Reviews (2019) Vol. 119, Iss. 18, pp. 10520-10594
Open Access | Times Cited: 760
Vector-based navigation using grid-like representations in artificial agents
Andrea Banino, Caswell Barry, Benigno Uría, et al.
Nature (2018) Vol. 557, Iss. 7705, pp. 429-433
Closed Access | Times Cited: 644
Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing
En Li, Liekang Zeng, Zhi Zhou, et al.
IEEE Transactions on Wireless Communications (2019) Vol. 19, Iss. 1, pp. 447-457
Open Access | Times Cited: 627
Reinforcement Learning, Fast and Slow
Matthew Botvinick, Sam Ritter, Jane X. Wang, et al.
Trends in Cognitive Sciences (2019) Vol. 23, Iss. 5, pp. 408-422
Open Access | Times Cited: 608
Deep learning for molecular design—a review of the state of the art
Daniel C. Elton, Zois Boukouvalas, Mark Fuge, et al.
Molecular Systems Design & Engineering (2019) Vol. 4, Iss. 4, pp. 828-849
Open Access | Times Cited: 596
Dynamic Key-Value Memory Networks for Knowledge Tracing
Jiani Zhang, Xingjian Shi, Irwin King, et al.
(2017), pp. 765-774
Open Access | Times Cited: 586
Unmasking Clever Hans predictors and assessing what machines really learn
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, et al.
Nature Communications (2019) Vol. 10, Iss. 1
Open Access | Times Cited: 578
Inferring and Executing Programs for Visual Reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, et al.
(2017)
Open Access | Times Cited: 524
Demystifying Parallel and Distributed Deep Learning
Tal Ben‐Nun, Torsten Hoefler
ACM Computing Surveys (2019) Vol. 52, Iss. 4, pp. 1-43
Closed Access | Times Cited: 500
Deep Reinforcement Learning: An Overview
Yuxi Li
arXiv (Cornell University) (2017)
Open Access | Times Cited: 485
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