Fetching the paper…
Reading the bibliography…
Neural Algorithmic Reasoning is an emerging area of machine learning which seeks to infuse algorithmic computation in neural networks, typically by training neural models to approximate steps of classical algorithms.
Maximal flow through a network
Lester Randolph Ford and Delbert R Fulkerson · 1956
Earlier work this paper cites.
On a routing problem
Richard Bellman · 1958
Earlier work this paper cites.
The traveling salesman problem: A duality approach
Mokhtar S Bazaraa and Jamie J Goode · 1977
Earlier work this paper cites.
A competitive (dual) simplex method for the assignment problem
Michel L Balinski · 1986
Earlier work this paper cites.
Learning to reason
Roni Khardon and Dan Roth · 1997
Earlier work this paper cites.
Convex optimization
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe · 2004
Earlier work this paper cites.
Introduction to algorithms, 3rd edition , pp. 1111–1117
Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, and Clifford Stein · 2009
Earlier work this paper cites.
Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
Earlier work this paper cites.
Flows in networks
Lester Randolph Ford Jr and Delbert Ray Fulkerson · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Neural programmer-interpreters
Scott Reed and Nando De Freitas · 2015
Earlier work this paper cites.
Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Cited alongside, same era.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro · 2017
Cited alongside, same era.
A gentle introduction to deep learning for graphs
Davide Bacciu, Federico Errica, Alessio Micheli, and Marco Podda · 2020
Later among the works it cites.
Neural bipartite matching
Dobrik Georgiev and Pietro Lió · 2020
Later among the works it cites.
What can neural networks reason about?
Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2020
Later among the works it cites.
Neural algorithmic reasoners are implicit planners
Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, and Mladen Nikolic · 2021
Later among the works it cites.
Whole brain vessel graphs: A dataset and benchmark for graph learning and neuroscience
Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul Büschl, Chinmay Prabhakar, Anjany Sekuboyina, Mihail I. Todorov, Georgios Kaissis, Ali Ertürk, Stephan Günnemann, and Bjoern H. Menze · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
On the use of the dual formulation for minimum weighted vertex cover in evolutionary algorithms
Mojgan Pourhassan, Tobias Friedrich, and Frank Neumann · 2017
Cited alongside, same era.
End-to-end differentiable proving
Tim Rocktäschel and Sebastian Riedel · 2017
Cited alongside, same era.
Relational inductive bias for physical construction in humans and machines
Jessica B. Hamrick, Kelsey R. Allen, Victor Bapst, Tina Zhu, Kevin R. McKee, Josh Tenenbaum, and Peter W. Battaglia · 2018
Cited alongside, same era.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
Cited alongside, same era.
Attention, learn to solve routing problems!
Wouter Kool, Herke van Hoof, and Max Welling · 2019
Cited alongside, same era.
Pointer graph networks
Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, and Charles Blundell
Cited in the paper.
Neural algorithmic reasoning
Petar Velickovic and Charles Blundell · 2021
Later among the works it cites.
Reasoning-modulated representations
Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, and Charles Blundell · 2021
Later among the works it cites.
How to transfer algorithmic reasoning knowledge to learn new algorithms?
Louis-Pascal A. C. Xhonneux, Andreea Deac, Petar Velickovic, and Jian Tang · 2021
Later among the works it cites.
The exact class of graph functions generated by graph neural networks
Mohammad Fereydounian, Hamed Hassani, Javid Dadashkarimi, and Amin Karbasi · 2022
Later among the works it cites.
A generalist neural algorithmic learner
Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Joseph Dudzik, Matko Bošnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, and Petar Veličković · 2022
Later among the works it cites.
The clrs algorithmic reasoning benchmark
Petar Veličković, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu, Andrea Banino, Misha Dashevskiy, Raia Hadsell, and Charles Blundell · 2022
Later among the works it cites.