Benchmarks
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.
Ranked #2 on
Language Modelling
on LAMBADA
Learning to Execute
• wojciechz/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.
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.