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Found 9 papers, 3 papers with code
SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions
In this work we propose an AutoML technique SapientML, that can learn from a corpus of existing datasets and their human-written pipelines, and efficiently generate a high-quality pipeline for a predictive task on a new dataset.
NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks
Aiming to make GNN improvements practical, this paper proposes an approach called NeuroBack, which builds on two insights: (1) predicting phases (i. e., values) of variables appearing in the majority (or even all) of the satisfying assignments are essential for CDCL SAT solving, and (2) it is sufficient to query the neural model only once for the predictions before the SAT solving starts.
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.
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.
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.
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.
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.
DeepRoad: GAN-based Metamorphic Autonomous Driving System Testing
In this paper, we propose DeepRoad, an unsupervised framework to automatically generate large amounts of accurate driving scenes to test the consistency of DNN-based autonomous driving systems across different scenes.
Software Engineering
Effectiveness of Anonymization in Double-Blind Review
Double-blind review relies on the authors' ability and willingness to effectively anonymize their submissions.
Digital Libraries General Literature Software Engineering
