5 citations · 9 across the 4 of their papers we have counts for
9 papers
Learning First-Order Rules with Differentiable Logic Program Semantics
Kun Gao, Katsumi Inoue, Yongzhi Cao +1
Learning first-order logic programs (LPs) from relational facts which yields intuitive insights into the data is a challenging topic in neuro-symbolic research. We introduce a nove…
Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming
Akihiro Takemura, Katsumi Inoue
We propose a method for generating explainable rule sets from tree-ensemble learners using Answer Set Programming (ASP). To this end, we adopt a decompositional approach where the…
Enhancing Linear Algebraic Computation of Logic Programs Using Sparse Representation
Tuan Nguyen Quoc, Katsumi Inoue, Chiaki Sakama
Algebraic characterization of logic programs has received increasing attention in recent years. Researchers attempt to exploit connections between linear algebraic computation and…
Partial Evaluation of Logic Programs in Vector Spaces
Chiaki Sakama, Hien D. Nguyen, Taisuke Sato +1
In this paper, we introduce methods of encoding propositional logic programs in vector spaces. Interpretations are represented by vectors and programs are represented by matrices.…
A 1.2-V 162.9-pJ/cycle Bitmap Index Creation Core with 0.31-pW/bit Standby Power on 65-nm SOTB
Xuan-Thuan Nguyen, Trong-Thuc Hoang, Hong-Thu Nguyen +2
The ability to maximize the performance during peak workload hours and minimize the power consumption during off-peak time plays a significant role in the energy-efficient systems.…
Exploiting Answer Set Programming with External Sources for Meta-Interpretive Learning
Tobias Kaminski, Thomas Eiter, Katsumi Inoue
Meta-Interpretive Learning (MIL) learns logic programs from examples by instantiating meta-rules, which is implemented by the Metagol system based on Prolog. Viewing MIL-problems a…