Fetching the paper…
Reading the bibliography…
In this paper, we study the OOD generalization of neural algorithmic reasoning tasks, where the goal is to learn an algorithm (e.g., sorting, breadth-first search, and depth-first search) from input-output pairs using deep neural networks.
Neural execution of graph algorithms
Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, and Charles Blundell · 1910
Earlier work this paper cites.
The reduction of a graph to canonical form and the algebra which appears therein
B. Weisfeiler and A. Leman · 1968
Earlier work this paper cites.
Petar Veličković, Lars Buesing, Matthew Overlan, Razvan Pascanu, Oriol Vinyals, and Charles Blundell · 2006
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron C. Courville · 2013
Earlier work this paper cites.
Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
Earlier work this paper cites.
Neural gpus learn algorithms
Lukasz Kaiser and Ilya Sutskever · 2016
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Shane Gu, and Ben Poole · 2017
Earlier work this paper cites.
Neural discrete representation learning
Aäron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
Earlier work this paper cites.
Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, and Alex Smola · 2017
Earlier work this paper cites.
Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
Earlier work this paper cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio’, and Yoshua Bengio · 2018
Earlier work this paper cites.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann Dauphin, and David Lopez-Paz · 2018
Earlier work this paper cites.
Martín Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
Earlier work this paper cites.
Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 2019
Earlier work this paper cites.
Towards a practical k-dimensional weisfeiler-leman algorithm
Christopher Morris and Petra Mutzel · 2019
Earlier work this paper cites.
Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
Earlier work this paper cites.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
Earlier work this paper cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Earlier work this paper cites.
Can graph neural networks count substructures?
Zhengdao Chen, Lei Chen, Soledad Villar, and Joan Bruna · 2020
Cited alongside, same era.
Coloring graph neural networks for node disambiguation
George Dasoulas, Ludovic Dos Santos, Kevin Scaman, and Aladin Virmaux · 2020
Cited alongside, same era.
Erdos goes neural: an unsupervised learning framework for combinatorial optimization on graphs
Nikolaos Karalias and Andreas Loukas · 2020
Cited alongside, same era.
Strong generalization and efficiency in neural programs
Yujia Li, Felix Gimeno, Pushmeet Kohli, and Oriol Vinyals · 2020
Cited alongside, same era.
Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2020
Cited alongside, same era.
Exploring length generalization in large language models
Cem Anil, Yuhuai Wu, Anders Johan Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Venkatesh Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, and Behnam Neyshabur · 2022
Closest in time.
Equivariant subgraph aggregation networks
Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, G. Balamurugan, Michael M. Bronstein, and Haggai Maron · 2022
Closest in time.
Improving graph neural network expressivity via subgraph isomorphism counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, and Michael M. Bronstein · 2022
Closest in time.
Learning causally invariant representations for out-of-distribution generalization on graphs
Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, and James Cheng · 2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Towards scale-invariant graph-related problem solving by iterative homogeneous graph neural networks
Hao Tang, Zhiao Huang, Jia-Yuan Gu, Bao-Liang Lu, and Hao Su · 2020
Cited alongside, same era.
Neural execution engines: Learning to execute subroutines
Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, and Milad Hashemi · 2020
Cited alongside, same era.
It’s not what machines can learn, it’s what we cannot teach
Gal Yehuda, Moshe Gabel, and Assaf Schuster · 2020
Cited alongside, same era.
Size-invariant graph representations for graph classification extrapolations
Beatrice Bevilacqua, Yangze Zhou, and Bruno Ribeiro · 2021
Cited alongside, same era.
The transformer network for the traveling salesman problem
Xavier Bresson and Thomas Laurent · 2021
Cited alongside, same era.
In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2021
Cited alongside, same era.
John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, and Ludwig Schmidt · 2021
Cited alongside, same era.
Gr’egoire Del’etang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Marcus Hutter, Shane Legg, and Pedro A. Ortega · 2022
Closest in time.
The role of permutation invariance in linear mode connectivity of neural networks
Rahim Entezari, Hanie Sedghi, Olga Saukh, and Behnam Neyshabur · 2022
Closest in time.
What functions can graph neural networks generate?
Mohammad Fereydounian, Hamed Hassani, and Amin Karbasi · 2022
Closest in time.
A generalist neural algorithmic learner
Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Abbana Bennani, R. Csordás, Andrew Dudzik, Matko Bovsnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, and Petar Veličković · 2022
Closest in time.
Learning the travelling salesperson problem requires rethinking generalization
Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, and Thomas Laurent · 2022
Closest in time.
Pure transformers are powerful graph learners
Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, and Seunghoon Hong · 2022
Closest in time.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
Closest in time.
Solving quantitative reasoning problems with language models
Aitor Lewkowycz, Anders Johan Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Venkatesh Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, and Vedant Misra · 2022
Closest in time.
Generalization analysis of message passing neural networks on large random graphs
Sohir Maskey, Ron Levie, Yunseok Lee, and Gitta Kutyniok · 2022
Closest in time.
Ordered subgraph aggregation networks
Chen Qian, Gaurav Rattan, Floris Geerts, Christopher Morris, and Mathias Niepert · 2022
Closest in time.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou · 2022
Closest in time.
Assaying out-of-distribution generalization in transfer learning
Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, and Francesco Locatello · 2022
Closest in time.
Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and Ludwig Schmidt · 2022
Closest in time.
Unveiling transformers with lego: a synthetic reasoning task
Yi Zhang, Arturs Backurs, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, and Tal Wagner · 2022
Closest in time.
Ood link prediction generalization capabilities of message-passing gnns in larger test graphs
Yangze Zhou, Gitta Kutyniok, and Bruno Ribeiro · 2022
Closest in time.