Search Results for author: Koh Takeuchi

Found 9 papers, 2 papers with code

Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations

no code implementations15 Dec 2021 Sein Minn, Jill-Jenn Vie, Koh Takeuchi, Hisashi Kashima, Feida Zhu

IKT's prediction of future student performance is made using a Tree-Augmented Naive Bayes Classifier (TAN), therefore its predictions are easier to explain than deep learning-based student models.

Knowledge Tracing

Crowdsourcing Evaluation of Saliency-based XAI Methods

no code implementations27 Jun 2021 Xiaotian Lu, Arseny Tolmachev, Tatsuya Yamamoto, Koh Takeuchi, Seiji Okajima, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima

In order to compare various saliency-based XAI methods quantitatively, several approaches for automated evaluation schemes have been proposed; however, there is no guarantee that such automated evaluation metrics correctly evaluate explainability, and a high rating by an automated evaluation scheme does not necessarily mean a high explainability for humans.

Inter-domain Multi-relational Link Prediction

1 code implementation11 Jun 2021 Luu Huu Phuc, Koh Takeuchi, Seiji Okajima, Arseny Tolmachev, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima

Multi-relational graph is a ubiquitous and important data structure, allowing flexible representation of multiple types of interactions and relations between entities.

Link Prediction

Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections

no code implementations19 Feb 2021 Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, Yoshinobu Kawahara

Extracting coherent patterns is one of the standard approaches towards understanding spatio-temporal data.

Succinct Trit-array Trie for Scalable Trajectory Similarity Search

1 code implementation21 May 2020 Shunsuke Kanda, Koh Takeuchi, Keisuke Fujii, Yasuo Tabei

To address this problem, we present the trajectory-indexing succinct trit-array trie (tSTAT), which is a scalable method leveraging LSH for trajectory similarity searches.

Localized Lasso for High-Dimensional Regression

no code implementations22 Mar 2016 Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski

We introduce the localized Lasso, which is suited for learning models that are both interpretable and have a high predictive power in problems with high dimensionality $d$ and small sample size $n$.

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