Learning to Execute
13 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Learning to Execute
Most implemented papers
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.
A Generalist Neural Algorithmic Learner
The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution.
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.
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
google-research/google-research
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NeurIPS 2020
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.
ProTo: Program-Guided Transformer for Program-Guided Tasks
sjtuytc/Neurips21-ProTo-Program-guided-Transformers-for-Program-guided-Tasks
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NeurIPS 2021
Furthermore, we propose the Program-guided Transformer (ProTo), which integrates both semantic and structural guidance of a program by leveraging cross-attention and masked self-attention to pass messages between the specification and routines in the program.
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics
ischubert/l2e
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NeurIPS 2021
Applications of Reinforcement Learning (RL) in robotics are often limited by high data demand.
Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions
google-research/runtime-error-prediction
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This presents an interesting machine learning challenge: can we predict runtime errors in a "static" setting, where program execution is not possible?
Learning to Execute Actions or Ask Clarification Questions
zhengxiangshi/learntoask
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Findings (NAACL) 2022
In this paper, we extend the Minecraft Corpus Dataset by annotating all builder utterances into eight types, including clarification questions, and propose a new builder agent model capable of determining when to ask or execute instructions.
The CLRS Algorithmic Reasoning Benchmark
deepmind/clrs
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Learning representations of algorithms is an emerging area of machine learning, seeking to bridge concepts from neural networks with classical algorithms.
