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). Our algorithm is based on the WFA learning algorithm by Balle and Mohri, which is in turn an extension of Angluin's classic \lstar algorithm. Our technical novelty is in the use of \emph{regression} methods for the so-called equivalence queries, thus exploiting the internal state space of an RNN to prioritize counterexample candidates. This way we achieve a quantitative/weighted extension of the recent work by Weiss, Goldberg and Yahav that extracts DFAs. We experimentally evaluate the accuracy, expressivity and efficiency of the extracted WFAs.Comment: AAAI 2020. We are preparing to distribute the implementatio

Lung Nodule Classification by the Combination of Fusion Classifier and Cascaded Convolutional Neural Networks

Lung nodule classification is a class imbalanced problem, as nodules are found with much lower frequency than non-nodules. In the class imbalanced problem, conventional classifiers tend to be overwhelmed by the majority class and ignore the minority class. We showed that cascaded convolutional neural networks can classify the nodule candidates precisely for a class imbalanced nodule candidate data set in our previous study. In this paper, we propose Fusion classifier in conjunction with the cascaded convolutional neural network models. To fuse the models, nodule probabilities are calculated by using the convolutional neural network models at first. Then, Fusion classifier is trained and tested by the nodule probabilities. The proposed method achieved the sensitivity of 94.4% and 95.9% at 4 and 8 false positives per scan in Free Receiver Operating Characteristics (FROC) curve analysis, respectively.Comment: Draft of ISBI2018. arXiv admin note: text overlap with arXiv:1703.0031

Answer Refinement Modification: Refinement Type System for Algebraic Effects and Handlers

Algebraic effects and handlers are a mechanism to structure programs with computational effects in a modular way. They are recently gaining popularity and being adopted in practical languages, such as OCaml. Meanwhile, there has been substantial progress in program verification via refinement type systems. However, thus far, there has not been a satisfactory refinement type system for algebraic effects and handlers. In this paper, we fill the void by proposing a novel refinement type system for algebraic effects and handlers. The expressivity and usefulness of algebraic effects and handlers come from their ability to manipulate delimited continuations, but delimited continuations also complicate programs' control flow and make their verification harder. To address the complexity, we introduce a novel concept that we call answer refinement modification (ARM for short), which allows the refinement type system to precisely track what effects occur and in what order when a program is executed, and reflect the information as modifications to the refinements in the types of delimited continuations. We formalize our type system that supports ARM (as well as answer type modification) and prove its soundness. Additionally, as a proof of concept, we have implemented a corresponding type checking and inference algorithm for a subset of OCaml 5, and evaluated it on a number of benchmark programs. The evaluation demonstrates that ARM is conceptually simple and practically useful. Finally, a natural alternative to directly reasoning about a program with delimited continuations is to apply a continuation passing style (CPS) transformation that transforms the program to a pure program. We investigate this alternative, and show that the approach is indeed possible by proposing a novel CPS transformation for algebraic effects and handlers that enjoys bidirectional (refinement-)type-preservation.Comment: 66 page