28 citations · 68 across the 18 of their papers we have counts for
18 papers
Controllable Lyrics-to-Melody Generation
Zhe Zhang, Yi Yu, Atsuhiro Takasu
Lyrics-to-melody generation is an interesting and challenging topic in AI music research field. Due to the difficulty of learning the correlations between lyrics and melody, previo…
An End-to-End Multi-Task Learning Model for Image-based Table Recognition
Nam Tuan Ly, Atsuhiro Takasu
Image-based table recognition is a challenging task due to the diversity of table styles and the complexity of table structures. Most of the previous methods focus on a non-end-to-…
TabIQA: Table Questions Answering on Business Document Images
Phuc Nguyen, Nam Tuan Ly, Hideaki Takeda +1
Table answering questions from business documents has many challenges that require understanding tabular structures, cross-document referencing, and additional numeric computations…
Rethinking Image-based Table Recognition Using Weakly Supervised Methods
Nam Tuan Ly, Atsuhiro Takasu, Phuc Nguyen +1
Most of the previous methods for table recognition rely on training datasets containing many richly annotated table images. Detailed table image annotation, e.g., cell or text boun…
MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction
Hung Nghiep Tran, Atsuhiro Takasu
Knowledge graph embedding aims to predict the missing relations between entities in knowledge graphs. Tensor-decomposition-based models, such as ComplEx, provide a good trade-off b…
Kernel Clustering with Sigmoid-based Regularization for Efficient Segmentation of Sequential Data
Tung Doan, Atsuhiro Takasu
Kernel segmentation aims at partitioning a data sequence into several non-overlapping segments that may have nonlinear and complex structures. In general, it is formulated as a dis…
Co-Factorization Model for Collaborative Filtering with Session-based Data
Binh Nguyen, Atsuhiro Takasu
Matrix factorization (MF) is a common method for collaborative filtering. MF represents user preferences and item attributes by latent factors. Despite that MF is a powerful method…
Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
Hung Nghiep Tran, Atsuhiro Takasu
Knowledge graph completion is an important task that aims to predict the missing relational link between entities. Knowledge graph embedding methods perform this task by representi…
Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space
Hung Nghiep Tran, Atsuhiro Takasu
The trends of open science have enabled several open scholarly datasets which include millions of papers and authors. Managing, exploring, and utilizing such large and complicated…
Boosting the Rating Prediction with Click Data and Textual Contents
ThaiBinh Nguyen, Atsuhiro Takasu
Matrix factorization (MF) is one of the most efficient methods for rating predictions. MF learns user and item representations by factorizing the user-item rating matrix. Further,…
Analyzing Knowledge Graph Embedding Methods from a Multi-Embedding Interaction Perspective
Hung Nghiep Tran, Atsuhiro Takasu
Knowledge graph is a popular format for representing knowledge, with many applications to semantic search engines, question-answering systems, and recommender systems. Real-world k…
Learning Representations from Product Titles for Modeling Shopping Transactions
Binh Nguyen, Atsuhiro Takasu
Shopping transaction analysis is important for understanding the shopping behaviors of customers. Existing models such as association rules are poor at modeling products that have…
NPE: Neural Personalized Embedding for Collaborative Filtering
ThaiBinh Nguyen, Atsuhiro Takasu
Matrix factorization is one of the most efficient approaches in recommender systems. However, such algorithms, which rely on the interactions between users and items, perform poorl…
Collaborative Item Embedding Model for Implicit Feedback Data
ThaiBinh Nguyen, Kenro Aihara, Atsuhiro Takasu
Collaborative filtering is the most popular approach for recommender systems. One way to perform collaborative filtering is matrix factorization, which characterizes user preferenc…
New and Improved Algorithms for Unordered Tree Inclusion
Tatsuya Akutsu, Jesper Jansson, Ruiming Li +2
The tree inclusion problem is, given two node-labeled trees and (the ``pattern tree'' and the ``target tree''), to locate every minimal subtree in (if any) that can be…
A Probabilistic Model for the Cold-Start Problem in Rating Prediction using Click Data
ThaiBinh Nguyen, Atsuhiro Takasu
One of the most efficient methods in collaborative filtering is matrix factorization, which finds the latent vector representations of users and items based on the ratings of users…
Partitioning Algorithms for Improving Efficiency of Topic Modeling Parallelization
Hung Nghiep Tran, Atsuhiro Takasu
Topic modeling is a very powerful technique in data analysis and data mining but it is generally slow. Many parallelization approaches have been proposed to speed up the learning p…
On the Parameterized Complexity of Associative and Commutative Unification
Tatsuya Akutsu, Takeyuki Tamura, Atsuhiro Takasu
This paper studies the unification problem with associative, commutative, and associative-commutative functions mainly from a viewpoint of the parameterized complexity on the numbe…