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Greatest papers with code

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks

NeurIPS 2020 google-research/google-research

More practically, we evaluate these models on the task of learning to execute partial programs, as might arise if using the model as a heuristic function in program synthesis.

LEARNING TO EXECUTE PROGRAM REPAIR SYSTEMATIC GENERALIZATION

Universal Transformers

ICLR 2019 tensorflow/tensor2tensor

Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all inputs in the sequence, leading to easy parallelization and faster training times.

LANGUAGE MODELLING LEARNING TO EXECUTE MACHINE TRANSLATION

Learning to Execute

17 Oct 2014wojciechz/learning_to_execute

Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are widely used because they are expressive and are easy to train.

CURRICULUM LEARNING LEARNING TO EXECUTE

Neural Execution Engines: Learning to Execute Subroutines

NeurIPS 2020 Yujun-Yan/Neural-Execution-Engines

A significant effort has been made to train neural networks that replicate algorithmic reasoning, but they often fail to learn the abstract concepts underlying these algorithms.

LEARNING TO EXECUTE