Learning to Execute
13 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Learning to Execute
Latest papers
Neural Execution Engines: Learning to Execute Subroutines
Yujun-Yan/Neural-Execution-Engines
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NeurIPS 2020
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.
Universal Transformers
tensorflow/tensor2tensor
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ICLR 2019
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.
Learning to Execute
wojciechz/learning_to_execute
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Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are widely used because they are expressive and are easy to train.
