7 citations · 10 across the 4 of their papers we have counts for
11 papers
Toward Neural-Network-Guided Program Synthesis and Verification
Naoki Kobayashi, Taro Sekiyama, Issei Sato +1
We propose a novel framework of program and invariant synthesis called neural network-guided synthesis. We first show that, by suitably designing and training neural networks, we c…
Gradual Typing for Extensibility by Rows
Taro Sekiyama, Atsushi Igarashi
This work studies gradual typing for row types and row polymorphism. Key ingredients in this work are the dynamic row type, which represents a statically unknown part of a row, and…
Weighted Automata Extraction from Recurrent Neural Networks via Regression on State Spaces
Takamasa Okudono, Masaki Waga, Taro Sekiyama +1
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, whi…
Dynamic Type Inference for Gradual Hindley--Milner Typing
Yusuke Miyazaki, Taro Sekiyama, Atsushi Igarashi
Garcia and Cimini study a type inference problem for the ITGL, an implicitly and gradually typed language with let-polymorphism, and develop a sound and complete inference algorith…
Handling polymorphic algebraic effects
Taro Sekiyama, Atsushi Igarashi
Algebraic effects and handlers are a powerful abstraction mechanism to represent and implement control effects. In this work, we study their extension with parametric polymorphism…
Reasoning about Polymorphic Manifest Contracts
Taro Sekiyama, Atsushi Igarashi
Manifest contract calculi, which integrate cast-based dynamic contract checking and refinement type systems, have been studied as foundations for hybrid contract checking. In this…