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The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution.
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Pointer graph networks
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Haiku: Sonnet for JAX, 2020
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From local structures to size generalization in graph neural networks
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Neural algorithmic reasoning
Petar Veličković and Charles Blundell · 2021
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Rodrigo Santa Cruz, Basura Fernando, Anoop Cherian, and Stephen Gould · 2017
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Graph networks as learnable physics engines for inference and control
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NerveNet: Learning structured policy with graph neural networks
Tingwu Wang, Renjie Liao, Jimmy Ba, and Sanja Fidler · 2018
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Relational inductive bias for physical construction in humans and machines
Jessica B Hamrick, Kelsey R Allen, Victor Bapst, Tina Zhu, Kevin R McKee, Joshua B Tenenbaum, and Peter W Battaglia · 2018
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Neural algorithmic reasoners are implicit planners
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Reasoning-modulated representations
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How to transfer algorithmic reasoning knowledge to learn new algorithms?
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Combinatorial optimization and reasoning with graph neural networks
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My body is a cage: the role of morphology in graph-based incompatible control
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MiniHack the planet: A sandbox for open-ended reinforcement learning research
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The CLRS algorithmic reasoning benchmark
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Introduction to algorithms
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