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Found 6 papers, 2 papers with code
Neural Program Repair by Jointly Learning to Localize and Repair
We show that it is beneficial to train a model that jointly and directly localizes and repairs variable-misuse bugs.
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning
By training Mo\"ET models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models.
A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)
However, MCML metrics based on model counting show that the performance can degrade substantially when tested against the entire (bounded) input space, indicating the high complexity of precisely learning these properties, and the usefulness of model counting in quantifying the true performance.
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
Embedding computation in molecular contexts incompatible with traditional electronics is expected to have wide ranging impact in synthetic biology, medicine, nanofabrication and other fields.
Programming and Training Rate-Independent Chemical Reaction Networks
Embedding computation in biochemical environments incompatible with traditional electronics is expected to have wide-ranging impact in synthetic biology, medicine, nanofabrication and other fields.
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees
We propose MoET, a more expressive, yet still interpretable model based on Mixture of Experts, consisting of a gating function that partitions the state space, and multiple decision tree experts that specialize on different partitions.
