🌱 My research interests are ML, DL, and NLP, especially multimodal in various domains such as language, images, audio, and tabular.

📫 How to reach me [email protected], Google Scholar

• Creation of physical simulation models
• Acceleration through ML replacement of physical simulation models

• Presented at the poster session of IBIS2022.
• Proposal of pre-trained models for tabular data
  • TabRet: Pre-training Transformer-based Tabular Models for Unseen Columns (link)
    • Accepted at the Workshop on Understanding Foundation Models at ICLR 2023.
    • Code: https://github.com/pfnet-research/tabret
    • The proposed method can be applied to data with different distributions during pre-training and fine-tuning in tabular data.

• Presented the research outcomes from the summer intern (2 months) at an internal company presentation.

• Developed a plug-in for DL application OSS (1 month).
• Research and development on efficient anomaly detection of graphic bugs in games (3 months).
• Research and development on frame interpolation for animation (3 months).
• Research and development on anomaly detection of rust and missing bottles in bridges (1 month).

Why Do Tree Ensemble Approximators Not Outperform the Recursive-Rule eXtraction Algorithm? (link) [March 2024]

• Accepted at Machine Learning and Knowledge Extraction.
• Code: https://github.com/somaonishi/InterpretableML-Comparisons

Rethinking Data Augmentation for Tabular Data in Deep Learning (link) [May 2023]

• arXiv.
• Code: https://github.com/somaonishi/MTR

TabRet: Pre-training Transformer-based Tabular Models for Unseen Columns (link) [March 2023]

• Accepted at the Workshop on Understanding Foundation Models at ICLR 2023.
• Work done during internship at Preferred Networks, Inc.
• Code: https://github.com/pfnet-research/tabret