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Found 13 papers, 4 papers with code
Learning Heuristics for Template-based CEGIS of Loop Invariants with Reinforcement Learning
As a concrete example, we implement the approach on top of PCSat, which is an invariant synthesizer based on template-based CEGIS.
Toward Neural-Network-Guided Program Synthesis and Verification
Another application is to a new program development framework called oracle-based programming, which is a neural-network-guided variation of Solar-Lezama's program synthesis by sketching.
Signature restriction for polymorphic algebraic effects
We propose signature restriction, a new notion to restrict the type signatures of operations, and show that signature restriction is sufficient to ensure type safety of an effectful language equipped with unrestricted polymorphic type assignment.
Programming Languages
Gradual Typing for Extensibility by Rows
Key ingredients in this work are the dynamic row type, which represents a statically unknown part of a row, and consistency for row types, which allows injecting static row types into the dynamic row type and, conversely, projecting the dynamic row type to any static row type.
Programming Languages
Weighted Automata Extraction from Recurrent Neural Networks via Regression on State Spaces
We present a method to extract a weighted finite automaton (WFA) from a recurrent neural network (RNN).
Handling polymorphic algebraic effects
Algebraic effects and handlers are a powerful abstraction mechanism to represent and implement control effects.
Programming Languages
Dynamic Type Inference for Gradual Hindley--Milner Typing
The DTI-based semantics not only avoids the divergence described above but also is sound and complete with respect to the semantics of fully instantiated terms in the following sense: if the evaluation of a term succeeds (i. e., terminates with a value) in the DTI-based semantics, then there is a fully instantiated version of the term that also succeeds in the explicitly typed blame calculus and vice versa.
Programming Languages Logic in Computer Science
Automated proof synthesis for propositional logic with deep neural networks
As an implementation of the estimator, we propose a proposition-to-proof architecture, which is a DNN tailored to the automated proof synthesis problem.
Profile-guided memory optimization for deep neural networks
We address this challenge by developing a novel profile-guided memory optimization to efficiently and quickly allocate memory blocks during the propagation in DNNs.
Lung Nodule Classification by the Combination of Fusion Classifier and Cascaded Convolutional Neural Networks
In this paper, we propose Fusion classifier in conjunction with the cascaded convolutional neural network models.
Bidirectional learning for time-series models with hidden units
Hidden units can play essential roles in modeling time-series having long-term dependency or on-linearity but make it difficult to learn associated parameters.
Towards Proof Synthesis Guided by Neural Machine Translation for Intuitionistic Propositional Logic
Inspired by the recent evolution of deep neural networks (DNNs) in machine learning, we explore their application to PL-related topics.
Multi-stage Neural Networks with Single-sided Classifiers for False Positive Reduction and its Evaluation using Lung X-ray CT Images
Lung nodule classification is a class imbalanced problem because nodules are found with much lower frequency than non-nodules.
