no code implementations • 26 Jul 2024 • Liangjie Sun, Wai-Ki Ching, Tatsuya Akutsu

In this paper, we mainly focus on three types of BNs with $n$ nodes (i. e., $K$-AND-OR-BNs, $K$-XOR-BNs, and $K$-NC-BNs, where $K$ is the number of input nodes for each node and NC means nested canalyzing) and study the upper and lower bounds of the number of observation nodes for these BNs.

no code implementations • 16 Dec 2023 • Tatsuya Akutsu, Avraham A. Melkman, Atsuhiro Takasu

We also show that a bag of $n$ decision trees can be represented by a bag of $T$ decision trees each with polynomial size if $n-T$ is a constant and a small classification error is allowed.

no code implementations • 27 Apr 2023 • Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

A novel framework for designing the molecular structure of chemical compounds with a desired chemical property has recently been proposed.

no code implementations • 13 Sep 2022 • Jianshen Zhu, Naveed Ahmed Azam, Shengjuan Cao, Ryota Ido, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

A set of graph theoretical descriptors in the feature function plays a key role to derive a compact formulation of such an MILP.

no code implementations • 21 Dec 2021 • Tatsuya Akutsu, Avraham A. Melkman

In this paper, we study the size and width of autoencoders consisting of Boolean threshold functions, where an autoencoder is a layered neural network whose structure can be viewed as consisting of an encoder, which compresses an input vector to a lower dimensional vector, and a decoder which transforms the low-dimensional vector back to the original input vector exactly (or approximately).

no code implementations • 24 Aug 2021 • Ryota Ido, Shengjuan Cao, Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

For this, we introduce a new way of representing a polymer as a form of monomer and define new descriptors that feature the structure of polymers.

no code implementations • 23 Aug 2021 • Naveed Ahmed Azam, Jianshen Zhu, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

In the framework, a chemical graph with a target chemical value is inferred as a feasible solution of a mixed integer linear program that represents a prediction function and other requirements on the structure of graphs.

no code implementations • 6 Jul 2021 • Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

In the framework, we first define a feature vector $f(C)$ of a chemical graph $C$ and construct an ANN that maps $x=f(C)$ to a predicted value $\eta(x)$ of a chemical property $\pi$ to $C$.

1 code implementation • 21 Sep 2020 • Naveed Ahmed Azam, Jianshen Zhu, Yanming Sun, Yu Shi, Aleksandar Shurbevski, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

In the second phase, given a target value $y^*$ of property $\pi$, a feature vector $x^*$ is inferred by solving an MILP formulated from the trained ANN so that $\psi(x^*)$ is close to $y^*$ and then a set of chemical structures $G^*$ such that $f(G^*)= x^*$ is enumerated by a graph search algorithm.

Data Structures and Algorithms Computational Engineering, Finance, and Science 05C92, 92E10, 05C30, 68T07, 90C11, 92-04

no code implementations • 21 Apr 2020 • Avraham A. Melkman, Sini Guo, Wai-Ki Ching, Pengyu Liu, Tatsuya Akutsu

An autoencoder is a layered neural network whose structure can be viewed as consisting of an encoder, which compresses an input vector of dimension $D$ to a vector of low dimension $d$, and a decoder which transforms the low-dimensional vector back to the original input vector (or one that is very similar).

no code implementations • 3 Jun 2014 • Hitoshi Koyano, Morihiro Hayashida, Tatsuya Akutsu

However, this non-one-to-one conversion involves a loss of information and makes it impossible to evaluate, using probability theory, the generalization error of a learning machine, considering that the given data to train and test the machine are strings generated according to probability laws.

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