238 citations · 254 across the 10 of their papers we have counts for
10 papers
Towards end-to-end ASP computation
Taisuke Sato, Akihiro Takemura, Katsumi Inoue
We propose an end-to-end approach for Answer Set Programming (ASP) and linear algebraically compute stable models satisfying given constraints. The idea is to implement Lin-Zhao's…
MatSat: a matrix-based differentiable SAT solver
Taisuke Sato, Ryosuke Kojima
We propose a new approach to SAT solving which solves SAT problems in vector spaces as a cost minimization problem of a non-negative differentiable cost function J^sat. In our appr…
A tensorized logic programming language for large-scale data
Ryosuke Kojima, Taisuke Sato
We introduce a new logic programming language T-PRISM based on tensor embeddings. Our embedding scheme is a modification of the distribution semantics in PRISM, one of the state-of…
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.…
Embedding Tarskian Semantics in Vector Spaces
Taisuke Sato
We propose a new linear algebraic approach to the computation of Tarskian semantics in logic. We embed a finite model M in first-order logic with N entities in N-dimensional Euclid…
A Logic-based Approach to Generatively Defined Discriminative Modeling
Taisuke Sato, Keiichi Kubota, Yoshitaka Kameya
Conditional random fields (CRFs) are usually specified by graphical models but in this paper we propose to use probabilistic logic programs and specify them generatively. Our inten…
Infinite probability computation by cyclic explanation graphs
Taisuke Sato, Philipp Meyer
Tabling in logic programming has been used to eliminate redundant computation and also to stop infinite loop. In this paper we investigate another possibility of tabling, i.e. to c…
Viterbi training in PRISM
Taisuke Sato, Keiichi Kubota
VT (Viterbi training), or hard EM, is an efficient way of parameter learning for probabilistic models with hidden variables. Given an observation , it searches for a state of hi…
Verbal Characterization of Probabilistic Clusters using Minimal Discriminative Propositions
Yoshitaka Kameya, Satoru Nakamura, Tatsuya Iwasaki +1
In a knowledge discovery process, interpretation and evaluation of the mined results are indispensable in practice. In the case of data clustering, however, it is often difficult t…
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
T. Sato, Y. Kameya
We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a…