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Found 11 papers, 1 papers with code
Probabilistic Black-Box Checking via Active MDP Learning
Our method, ProbBBC, refines the conventional BBC approach by (1) employing an active Markov Decision Process (MDP) learning method during the learning phase, (2) incorporating probabilistic model checking in the synthesis phase, and (3) applying statistical hypothesis testing in the validation phase.
BOREx: Bayesian-Optimization--Based Refinement of Saliency Map for Image- and Video-Classification Models
We propose a new black-box method BOREx (Bayesian Optimization for Refinement of visual model Explanation) to refine a heat map produced by any method.
Formalizing Statistical Causality via Modal Logic
We propose a formal language for describing and explaining statistical causality.
Sound and Relatively Complete Belief Hoare Logic for Statistical Hypothesis Testing Programs
We propose a new approach to formally describing the requirement for statistical inference and checking whether a program uses the statistical method appropriately.
HELMHOLTZ: A Verifier for Tezos Smart Contracts Based on Refinement Types
Due to the large amount of money that smart contracts deal with, there is a surging demand for a method that can statically and formally verify them.
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.
Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs
We present a novel sampling framework for probabilistic programs.
Visualizing Color-wise Saliency of Black-Box Image Classification Models
Several techniques have been proposed to address this problem; one of which is RISE, which explains a classification result by a heatmap, called a saliency map, which explains the significance of each pixel.
A Contract Corpus for Recognizing Rights and Obligations
Therefore, a language-processing system that can present information concerning rights and obligations found within a given contract document would help a contracting party to make better decisions.
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.
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.
