Biography

Shunsuke Kitada (北田 俊輔 in Japanese) is a Ph.D student at Major in applied informatics, graduate school of science and engineering, Hosei University under the supervision of Prof. Hitoshi Iyatomi. His research interests include deep learning-based natural language processing, computer vision, medical image processing, and computational advertising.

His personality traits can be summarized as follows:

  • ❤️ Love research and development. He is enjoying research life, and he is currently focusing on three research topics simultaneously, e.g., natural language processing, medical image-based computer vision, computational advertising.
  • 📝 Every day read and implement the SoTA models. He has released many re-implementations of models using mainly Chainer and PyTorch. Therefore, based on state-of-the-art cases, he can advise on deep learning-based product design.
  • 😄 High technical communicativity. Summarize what he made and what he studied, and spread such information. This shows that he can input and output regularly.

The resume is available in PDF .

Interests
  • 🤖 Natural Language Processing
  • 💻 Computer Vision
  • 🏥 Medical Image Processing
  • 📃 Computational Advertising
Education

  • PhD in Engineering, Current

    Graduate School of Science and Engineering, Hosei University

  • MSc in Engineering, 2020

    Graduate School of Science and Engineering, Hosei University

  • BSc in Engineering, 2018

    Hosei University

Recent News 😀

All news »

International Journal and Conference 📝

All journal and conference papers ».

Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation
Proc. of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop
PDF Preprint Code Video
Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation
AraDIC: Arabic Document Classification using Image-Based Character Embeddings and Class-Balanced Loss
Proc. of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
PDF Video DOI Preprint Code
AraDIC: Arabic Document Classification using Image-Based Character Embeddings and Class-Balanced Loss
Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative
Proc. of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019. (Acceptance Rate = 20%; 1st place in Data Mining & Analysis at Google Scholar Metrics)
PDF DOI Preprint Code Video
Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative
End-to-End Text Classification via Image-based Embedding using Character-level Networks
Proc. of IEEE Applied Imagery Pattern Recognition (AIPR) 2018 Workshop
PDF DOI Preprint
End-to-End Text Classification via Image-based Embedding using Character-level Networks

Preprints 📝

All preprint papers »

Attention Meets Perturbations: Robust and Interpretable Attention with Adversarial Training
In recent years, deep learning models have placed more emphasis on the interpretability and robustness of models. The attention …
Preprint Code
Attention Meets Perturbations: Robust and Interpretable Attention with Adversarial Training
Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning
International Skin Imaging Collaboration 2018.
Preprint
Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning

Domestic Conference in Japanese 📝

All domestic conference papers »

大畑 和也, 長澤 駿太, 北田 俊輔 (2021). キャプション生成時低品質データ事前検知の試み. NLP 2021.

PDF

北田 俊輔, 彌冨 仁 (2021). 半教師あり文書分類のための仮想敵対的学習による注意機構の頑健性および解釈性の向上. NLP 2021.

PDF

北田 俊輔, 彌冨 仁, 関 喜史 (2020). 生存時間分析を用いた広告クリエイティブの停止予測. JSAI 2020.

PDF JSAI

北田 俊輔, 彌冨 仁 (2020). 解釈可能な敵対的摂動を用いた頑健な注意機構の学習. NLP 2020.

PDF

青木 匠, 北田 俊輔, 彌冨 仁 (2020). 文字単位の解釈可能な潜在表現のdata augmentation. NLP 2020.

PDF

Mahmoud Daif, Shunsuke Kitada, Hitoshi Iyatomi (2020). Image-based Character Embedding for Arabic Document Classification. NLP 2020.

PDF

長澤 駿太, 北田 俊輔, 彌冨 仁 (2020). Script-aware embedding を用いた文字表現の獲得. NLP 2020.

PDF

北田 俊輔, 彌冨 仁 (2019). 解釈性向上のための注意機構と損失勾配に対する関連損失の導入. YANS 2019.

Mahmoud Daif, Shunsuke Kitada, Hitoshi Iyatomi (2019). Image Based Character Embeddings for Arabic Document Classification. YANS 2019.

Slides

長澤 駿太, 北田 俊輔, 彌冨 仁 (2019). 日本語の文字体系を考慮した文書分類モデルの提案. YANS 2019.

Poster Slides

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Recent & Upcoming Talks 🎙️

All recent and upcoming talks »

Recent Issues in Intelligent Robotics, Machine Learning, and Distributed System
This webinar introduces advanced research projects on intelligent robotics, Machine Learning and Distributed System under the joint auspices of the engineering faculties of University of Carthage and Hosei University, IIST: Institute of Integrated Science and Technology, to promote international research and educational collaboration among their affiliated schools.
Mar 17, 2021 6:45 PM — 7:55 PM Online
Recent Issues in Intelligent Robotics, Machine Learning, and Distributed System
The Present and Future of Machine Learning for Ad Creatives
In this talk, I will introduce my research topic: the framework of ad creative evaluation based on CVR prediction to support the creation of effective ad creative. Also, I will introduce several research topics related to the latest ad technology and discuss the current status and prospects achieved by the research on ad creative and machine learning techniques.
Jun 9, 2020 2:40 PM — 3:00 PM Kumamoto-Jo Hall
Slides Organized session page Presentation information
The Present and Future of Machine Learning for Ad Creatives
Text Analytics Symposium 2019
This talk introduces a case study of research and development internship at Gunosy Inc.
Sep 27, 2019 3:40 PM — 4:00 PM Future Corporation
PDF Slides IEICE
Text Analytics Symposium 2019
CCSE 2019
On Predicting Ad Creative Evaluation using Deep Learning
Jul 13, 2019 4:05 PM — 4:20 PM Ito Hall, The University of Tokyo
CCSE 2019
ICML/KDD 2019 Pre-conference session
Jun 3, 2019 7:00 PM — 9:30 PM LAPRAS Inc.
ICML/KDD 2019 Pre-conference session

Awards & Grants 🏆

All awards and grants »

Featured Post 📝

Last updated on Apr 9, 2021 DC
日本学術振興会特別研究員 (DC2) 申請書・学振
日本学術振興会特別研究員 (DC2) 申請書・学振

補欠繰り上げで 2021 年度学振特別研究員 DC2 に採用していただいた際の申請書です。

Posts & Articles 📝

about awards and interviews (almost in 🇯🇵)

北田俊輔さん (D2), 日本学術振興会特別研究員に
https://ai.ws.hosei.ac.jp/wp/news/news20210408/
Last updated on Apr 9, 2021 Interview
北田俊輔さん (D2), 日本学術振興会特別研究員に
理工学研究科の北田俊輔氏が日本学術振興会特別研究員 (DC2) に内定
https://www.hosei.ac.jp/koganei/pickup/article-20210305121330/
Last updated on Apr 9, 2021 Interview
理工学研究科の北田俊輔氏が日本学術振興会特別研究員 (DC2) に内定
データマイニング研究、グノシーがインターン院生主力に
https://www.nikkan.co.jp/articles/view/00546182 and https://newswitch.jp/p/20932
Last updated on Apr 9, 2021 Interview
データマイニング研究、グノシーがインターン院生主力に
理工学研究科在学生の北田俊輔氏が「奨励賞」を受賞
https://www.hosei.ac.jp/gs/NEWS/zaigaku/koganei/20190920/?auth=9abbb458a78210eb174f4bdd385bcf54
Last updated on Apr 9, 2021 Award
理工学研究科在学生の北田俊輔氏が「奨励賞」を受賞
理工学研究科在学生の北田俊輔氏が「学生奨励賞」を受賞
https://www.hosei.ac.jp/gs/NEWS/topics/jusho/190411_4/?auth=9abbb458a78210eb174f4bdd385bcf54
Last updated on Apr 9, 2021 Award
理工学研究科在学生の北田俊輔氏が「学生奨励賞」を受賞
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Projects

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All Official Implementation PyTorch Chainer Tool

AllenNLP Eraser

Collection of AllenNLP DatasetReaders for ERASER

Code

Attention Meets Perturbation

Official Implementation of the paper Attention Meets Perturbations: Robust and Interpretable Attention with Adversarial Training

Code PDF

Human Attention Map for Text Classification

Reimplementation of the paper Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words? (ACL2020) in PyTorch

Code
Paper Survey

Paper Survey

📚Survey of previous research and related works on machine learning (especially Deep Learning) in Japanese.

Code
Hosei University Soft Tennis Club Hp

Hosei University Soft Tennis Club Hp

🎾 Hosei University Soft Tennis Club’s home page repository.

Follow Code

chainer-FocalLoss

Implementation of Focal loss in Chainer.

Code

chainer-RICAP

Implementation of RICAP in Chainer.

Code

LSUV.pytorch

Implementation of LSUV (Layer-sequential unit-variance) in PyTorch.

Code Code

libtorch-gin-api-server

High-speed Deep learning API Server with Libtorch (C++) and Gin (Golang)

Code

nvhtop

A tool for enriching the output of nvidia-smi forked from peci1/nvidia-htop.

Code

chainer-MeanTeachers

Implementation of Mean teachers are better role models in Chainer.

Code

chainer-PyramidNet

Implementation of PyramidNet in Chainer.

Code

chainer-InceptionResNetV2

Implementation of InceptionResNetV2 in Chainer.

Code

chainer-IMSAT

Implementation of IMSAT in Chainer.

Code

chainer-center-loss

Implementation of Center Loss in Chainer.

Code

Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creatives

Official Implementation of the paper Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creatives

Code PDF

Popular Topics 📚

AllenNLP Award Chainer Computational Advertising DC Domestic Conference HP Image-based Character Embedding International Conference Interpretability Interview Invited Speaker Invited talk JSPS Medical Image Processing Natural Language Processing Non-refereed Official Implementation PyTorch Refereed