20 citations · 57 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
Automated proof synthesis for propositional logic with deep neural networks
Taro Sekiyama, Kohei Suenaga
This work explores the application of deep learning, a machine learning technique that uses deep neural networks (DNN) in its core, to an automated theorem proving (ATP) problem. T…
Profile-guided memory optimization for deep neural networks
Taro Sekiyama, Takashi Imamichi, Haruki Imai +1
Recent years have seen deep neural networks (DNNs) becoming wider and deeper to achieve better performance in many applications of AI. Such DNNs however require huge amounts of mem…