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2006.08084
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Neural Execution Engines: Learning to Execute Subroutines
Neural Information Processing Systems (NeurIPS), 2020
15 June 2020
Yujun Yan
Kevin Swersky
Danai Koutra
Parthasarathy Ranganathan
Milad Hashemi
NAI
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Papers citing
"Neural Execution Engines: Learning to Execute Subroutines"
35 / 35 papers shown
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Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning
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On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methods
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Simulation of Graph Algorithms with Looped Transformers
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Latent Space Representations of Neural Algorithmic Reasoners
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Parallel Algorithms Align with Neural Execution
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Neural Algorithmic Reasoning Without Intermediate Supervision
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Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning
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Mrinmaya Sachan
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Code Execution with Pre-trained Language Models
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Neuro-Symbolic Execution of Generic Source Code
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Jin Tian
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259
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Learnable Commutative Monoids for Graph Neural Networks
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381
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Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks
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The CLRS Algorithmic Reasoning Benchmark
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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
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Xuezhi Wang
Dale Schuurmans
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481
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Milad Hashemi
Kevin Swersky
Yaoqing Yang
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636
299
0
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Pointer Graph Networks
Neural Information Processing Systems (NeurIPS), 2020
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Lars Buesing
Matthew Overlan
Razvan Pascanu
Oriol Vinyals
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379
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11 Jun 2020
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