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Found 6 papers, 3 papers with code
Rotation Invariant Quantization for Model Compression
Post-training Neural Network (NN) model compression is an attractive approach for deploying large, memory-consuming models on devices with limited memory resources.
Demonstration Informed Specification Search
This paper considers the problem of learning temporal task specifications, e. g. automata and temporal logic, from expert demonstrations.
Learning Branching Heuristics for Propositional Model Counting
In addition to step count improvements, Neuro# can also achieve orders of magnitude wall-clock speedups over the vanilla solver on larger instances in some problem families, despite the runtime overhead of querying the model.
Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning
We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning.
Learning Heuristics for Automated Reasoning through Reinforcement Learning
We demonstrate how to learn efficient heuristics for automated reasoning algorithms through deep reinforcement learning.
Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning
We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning.
