ABCI利用者による研究成果
成果の公表について（約款21条関連）
研究成果等の報告
約款第21条に定める通り、ABCIを利用して得られた成果を学会発表、国際会議発表、プレスリリース等で発表する場合は、事前にその情報（表題、著者名、概要、会議名称・雑誌名称、発表日等）を以下のメールアドレスまでお知らせください。
論文等への謝辞の記載
ABCIの利用によって得られた成果を学会発表、国際会議発表、論文、プレスリリース等で公表する場合は、謝辞の記載をお願いします。以下は謝辞の文例です。
開発加速利用の場合の文例：
- 日本語: 産総研及びAIST Solutionsが提供するABCI 3.0を「ABCI 3.0開発加速利用」を支援を受けて利用した．
- 英語: We used ABCI 3.0 provided by AIST and AIST Solutions with support from “ABCI 3.0 Development Acceleration Use”.
開発加速利用以外の場合の文例：
- 日本語: 産総研及びAIST Solutionsが提供するABCI 3.0を利用した．
- English: We used ABCI 3.0 provided by AIST and AIST Solutions.
論文等への参考文献の記載（任意）
ABCIを利用して得られた成果に関して学会発表、国際会議発表、論文等でABCIを引用する場合には、以下の文献情報を利用してください。
@misc{takano2024abci30evolutionleading,
title={ABCI 3.0: Evolution of the leading AI infrastructure in Japan},
author={Ryousei Takano and Shinichiro Takizawa and Yusuke Tanimura and Hidemoto Nakada and Hirotaka Ogawa},
year={2024},
eprint={2411.09134},
archivePrefix={arXiv},
primaryClass={cs.NI},
url={https://arxiv.org/abs/2411.09134},
}
研究成果（ABCIグランドチャレンジ）
論文
- Kates-Harbeck, J., Svyatkovskiy, A., Tang, W., “Predicting disruptive instabilities in controlled fusion plasmas through deep learning”, Nature (2019 Apr).
国際会議・学会発表
- H. Kataoka, R. Hayamizu, R. Yamada, K. Nakashima, S. Takashima, X. Zhang, E.J. Martinez-Noriega, N. Inoue, R. Yokota, “Replacing Labeled Real-Image Datasets With Auto-Generated Contours”, CVPR 2022, Jun. 2022.
- Osawa, K., Swaroop, S., Jain, A., Eschenhagen,R., Turner, R. E., Yokota, R., Khan, M. E., “Practical Deep Learning with Bayesian Principles”, The 33rd Conference on Neural Information Processing Systems (NeurIPS 2019).
- M. Yamazaki, “Yet Another Accelerated SGD: ResNet-50 Training in 70.4 sec.”, 8th ADAC Workshop, Oct. 2019.
- T. Narihara, H. Suganuma, “Sony’s deep learning development environment for massively large scale training”, 8th ADAC Workshop, Oct. 2019.
- Y. Tsuji, K. Osawa, Y. Ueno, A. Naruse, R. Yokota, and S. Matsuoka, “Performance Optimizations and Analysis of Distributed Deep Learning with Approximated Second-Order Optimization Method”, The 1st Workshop on Parallel and Distributed Machine Learning 2019 (PDML19).
- Y. Ueno and R. Yokota, “Hierarchical Topology-aware Communication for Scaling Deep Learning to Thousands of GPUs”, The 19th Annual IEEE/ACM International Symposium in Cluster, Cloud, and Grid Computing (CCGrid 2019).
- K. Osawa, Y. Tsuji, Y. Ueno, A. Naruse, T. Yokota, and S. Matsuoka, “Large-scale Distributed Second-order Optimization Using Kronecker-factored Approximate Curvature for Deep Convolutional Neural Networks”, Conference on Computer Vision and Pattern Recognition (CVPR 2019).
- Yokota, R. “Kronecker Factorization for Second Order Optimization in Deep Learning”, SIAM CSE, Spokane, USA, February 25-March 3 (2019).
- Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash (arXiv:1811.05233) [v2] Tue, 5 Mar 2019.
- ImageNet/ResNet-50 Training in 224 Seconds (arXiv:1811.05233) [v1] Tue, 13 Nov 2018.
プレスリリース
研究成果（ABCI一般利用等）
プレスリリース
国際会議等
- Chikashi Shinagawa, So Takamoto, Daiki Shintani, Katsuhiko Nishimura, Ju Li, “Towards r2SCAN-level Universal Neural Network Potential for Materials Discovery”, 2024 MRS Fall Meeting & Exhibit, Dec 2024
- So Takamoto, Chikashi Shinagawa, Daiki Shintani, Katsuhiko Nishimura, Ju Li, “Support for 96 Elements and Improved Robustness of Universal Neural Network Potential PFP”, 2024 MRS Fall Meeting & Exhibit, Dec 2024
- Gaku Morio, Christopher D. Manning, “An NLP Benchmark Dataset for Assessing Corporate Climate Policy Engagement”, NeurIPS 2023 Track on Datasets and Benchmarks, Dec 2023
- Shun Muroga, “Multimodal Deep Learning for Composite Materials”, MRM2023/IUMRS-ICA2023 Materials Innovation for Sustainable Development Goals, Dec 2023
- Yosuke Yamaguchi, Zhao Wang, Yuusuke Nakano, Jun Ohya, Katsuya Hasegawa, “A Chronological and Cooperative Route Optimization Method for Heterogeneous Vehicle Routing Problem”, The 25th Int’l Conf on Artificial Intelligence, Jul 2023
- Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi, Yasuhiro Sogawa, “Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic”, Proceedings of the 40th International Conference on Machine Learning, (ICML 2023), Jul 2023
- Yosuke Oyama, Akihiro Tabuchi and Atsushi Tokuhisa, “Accelerating AlphaFold2 Inference of Protein Three-Dimensional Structure on the Supercomputer Fugaku”, 13th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures, Jun 2023
- Yosuke Oyama, Takumi Honda, Atsushi Ishikawa and Koichi Shirahata, “Accelerating Hybrid DFT Simulations Using Performance Modeling on Supercomputers”, The 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2023), May 2023
- Minori Furukori, Yasushi Nagamune, Yasuo Nakayama, Takuya Hosokai, “High-throughput transient photoluminescence spectrometer for deep learning of thermally activated delayed fluorescence materials”, Journal of Materials Chemistry C, 2023
- I. Suemitsu, H. K. Bhamgara, K. Utsugi, J. Hashizume and K. Ito, “Fast Simulation-Based Order Sequence Optimization Assisted by Pre-Trained Bayesian Recurrent Neural Network”, IEEE Robotics and Automation Letters, vol. 7, no. 3, 2023
- Akifu Saptarshi Sinha, Hiroki Ohashi, and Katsuyuki Nakamura, “Class-Difficulty Based Methods for Long-Tailed Visual Recognition”, International Journal of Computer Vision, 2023
- Shun Muroga, Yasuaki Miki, Kenji Hata, “A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials”, arXiv:2303.16412, 29, Mar 2023
- Masaru Koido, Chung-Chau Hon, Satoshi Koyama, Hideya Kawaji, Yasuhiro Murakawa, Kazuyoshi Ishigaki, Kaoru Ito, Jun Sese, Nicholas F. Parrish, Yoichiro Kamatani, Piero Carninci & Chikashi Terao, “Prediction of the cell-type-specific transcription of non-coding RNAs from genome sequences via machine learning”, Nature Biomedical Engineering, Nov 2022
- Masato Tamura, Rahul Vishwakarma, Ravigopal Vennelakanti, “Hunting Group Clues with Transformers for Social Group Activity Recognition”, European Conference on Computer Vision (ECCV) 2022, Oct 2022
- Jana Backhus, Yasutaka Kono, “Cooling power dependency simulation for real-world data center data”, SoftCOM 2022, Sep 2022
- I. Suemitsu, H. K. Bhamgara, K. Utsugi, J. Hashizume and K. Ito, “Fast Simulation-Based Order Sequence Optimization Assisted by Pre-Trained Bayesian Recurrent Neural Network”, IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7818-7825, Jul 2022
- Gaku Morio, Hiroaki Ozaki, Atsuki Yamaguchi, Yasuhiro Sogawa, “Hitachi at SemEval-2022 Task 10: Comparing Graph- and Seq2Seq-based Models Highlights Difficulty in Structured Sentiment Analysis”, International Workshop on Semantic Evaluation (SemEval-2022), Jul 2022
- Yosuke Oyama, Koichi Shirahata, “Optimizing Open-Source CFD Software on a GPU Supercomputer”, WCCM-APCOM YOKOHAMA 2022, Jul 2022
- Shun Muroga, Yasuaki Miki, Takashi Honda, Hiroshi Morita, Toshiya Okazaki, Kenji Hata, “Multimodal Artificial Intelligence for Virtual Screening of Complex Nanocomposite Materials”, 2022 MRS Fall Meeting, Dec 2022
- Yosuke Oyama, Koichi Shirahata, “Optimizing Open-Source CFD Software on a GPU Supercomputer”, WCCM-APCOM YOKOHAMA 2022, Jul 2022
- Haderbache Amir, Ohno Yoshinobu, Takahiro Miyashiro, Yasuhiro Goto, Koichi Shirahata, Hiroshi Okuda, “Performance Analysis of 3D Ground Application for Next-Generation Supercomputers”, WCCM-APCOM 2022, Jul 2022
- Shunsuke Onuma, Kazuma Kadowaki, “JRIRD at the NTCIR-16 FinNum-3 Task: Investigating the Effect of Numerical Representations in Manager‘s Claim Detection”, NTCIR-16, Jun 2022
- Tatsuya Ishigaki, Suzuko Nishino, Sohei Washino, Hiroki Igarashi, Yukari Nagai, Yuichi Washida and Akihiko Murai, “Automating Horizon Scanning in Future Studies”, 13th Conference on Language Resources and Evaluation (LREC2022), Jun 2022
- Hideyuki Ichiwara, Hiroshi Ito, Kenjiro Yamamoto, Hiroki Mori, and Tetsuya Ogata, “Multimodal Time Series Learning of Robots Based on Distributed and Integrated Modalities: Verification with a Simulator and Actual Robots”, ICRA 2023, May 2023
- Hideyuki Ichiwara, Hiroshi Ito, Kenjiro Yamamoto, Hiroki Mori and Tetsuya Ogata, “Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility”, ICRA2022, May 2022
- Hiroshi Ito, Hideyuki Ichiwara, Kenjiro Yamamoto, Hiroki Mori and Tetsuya Ogata, “Integrated Learning of Robot Motion and Sentences: Real-Time Prediction of Grasping Motion and Attention based on Language Instructions”, ICRA2022, May 2022
- Taku Monjo, Masaru Koido, Satoi Nagasawa, Yutaka Suzuki and Yoichiro Kamatani, “Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation”, Scientific Reports, Mar 2022
- Shanshan Liu, Tatsuya Ishigaki, Yui Uehara, Hiroya Takamura, Chowdhury Mohammad Mahir Asef, Mutsunori Uenuma, Hiroyuki Shindo, Yuji Matsumoto, ”A Generative Approach for End-to-End Relation Extraction”, SCIDOCA 2021, Nov 2021
- Yumi Hamazono, Tatsuya Ishigaki, Yusuke Miyao, Hiroya Takamura, Ichiro Kobayashi, “Unpredictable Attributes in Market Comment Generation ”, The 35th Pacific Asia Conference on Language, Information and Computation(PACLIC35), Nov 2021
- GYoichi Hirose, Nozomu Yoshinari, Shinichi Shirakawa, “NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameters”, The 13th Asian Conference on Machine Learning (ACML 2021), Nov 2021
- Guanqun Ding, Nevrez Imamoglu, Ali Caglayan, Masahiro Murakawa, Ryosuke Nakamura, “FBR-CNN: A Feedback Recurrent Network for Video Saliency Detection”, IEEE International Workshop on Machine Learning for Signal Processing (IEEE MLSP 2021), Oct 2021
- S. Matsumori, K. Shingyouchi, Y. Abe, Y. Fukuchi, K. Sugiura, and M. Imai, “Unified Questioner Transformer for Descriptive Question Generation in Goal-Oriented Visual Dialogue”, The International Conference on Computer Vision 2021 (ICCV 2021), Oct 2021
- Tatsuya Ishigaki, Goran Topic, Yumi Hamazono, Hiroshi Noji, Ichiro Kobayashi, Yusuke Miyao, Hiroya Takamura, ”Generating Racing Game Commentary from Vision, Language, and Structured Data”, The 14th International Conference on Natural Language Generation (INLG2021), Sep 2021
- Shintaro Yamamoto, Anne Lauscher, Simone Paolo Ponzetto, Goran Glavaš, Shigeo Morishima, “Visual Summary Identification From Scientific Publications via Self-Supervised Learning”, Frontiers in Research Metrics and Analytics, Aug 2021
- Shinichiro Takizawa, Yusuke Tanimura, Hidemoto Nakada, Ryousei Takano, Hirotaka Ogawa, “ABCI 2.0: Advances in Open AI Computing Infrastructure at AIST”, IPSJ SIG Technical Reports HPC-180, Jul 2021
- Akifumi Suzuki, Hiroaki Akutsu, Takahiro Naruko, Koki Tsubota, Kiyoharu Aizawa, “Learned Image Compression with Super-Resolution Residual Modules and DISTS Optimization”, CVPR 2021, June 2021
- Hiroaki Yamada, Masataka Shirahashi, Naoyuki Kamiyama, Yumeka Nakajima, “MAS Network: Surrogate Neural Network for Multi-Agent Simulation”, The 22nd International Workshop on Multi-Agent-Based Simulation, 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS2021), May 2021
- Shintaro Yamamoto, Anne Lauscher, Simone Paolo Ponzetto, Goran Glavaš and Shigeo Morishima, “Self-Supervised Learning for Visual Summary Identification in Scientific Publications” 11th International Workshop on Bibliometric-enhanced Information Retrieval, Apr 1, 2021
- Wassapon Watanakeesuntorn, Keichi Takahashi, Kohei Ichikawa, Joseph Park, George Sugihara, Ryousei Takano, Jason Haga, Gerald M. Pao, “Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution”, the 26th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2020).
- Yue Qiu, Yutaka Satoh, Ryota Suzuki, Kenji Iwata, Hirokatsu Kataoka, “Indoor Scene Change Captioning based on Multi-modality Data”, Sensors, 2020.
- Yuchi Ishikawa, Seito Kasai, Yoshimitsu Aoki, Hirokatsu Kataoka, “Alleviating Over-segmentation Errors by Detecting Action Boundaries,” Winter Conference on Applications of Computer Vision (WACV), 2021.
- Nakamasa Inoue, Eisuke Yamagata, Hirokatsu Kataoka, “Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data,” International Conference on Pattern Recognition (ICPR), 2020.
- Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, Yutaka Satoh, “Pre-training without Natural Images”, Asian Conference on Computer Vision (ACCV), 2020. The Best Paper Award.
- Hideki Tsunashima, Hirokatsu Kataoka, Junji Yamato, Qiu Chen, Shigeo Morishima, “Adversarial Knowledge Distillation for a Compact Generator”, International Conference on Pattern Recognition (ICPR), 2020.
- Atsushi Ikeda, Hirokazu Nosato, Yuta Kochi, Hiromitsu Negoro, Takahiro Kojima, Hidenori Sakanashi, Masahiro Murakawa, and Hiroyuki Nishiyama, “Cystoscopic imaging for bladder cancer detection based on stepwise organic transfer learning with a pre-trained convolutional neural network,” Journal of Endourology.
- Yusuke Tanimura, Shinichiro Takizawa, Hirotaka Ogawa, “Building and Evaluation of Cloud Storage and Datasets Services on AI and HPC Converged Infrastructure”, The 9th Workshop on Scalable Cloud Data Management, co-located with 2020 IEEE International Conference on Big Data, Dec 2020
- Hinari Daido, “New automated theorem prover for a fragment of Dependent Type Semantics (DTS)”, Logic and Engineering of Natural Language Semantics 17 (LENLS17), November 15-16, 2020.
- Motoki Taniguchi and others, “A Large-Scale Corpus of E-mail Conversations with Standard and Two-Level Dialogue Act Annotations”, The 28th International Conference on Computational Linguistics (COLING2020) December 8-11, 2020.
- Yui Uehara, Tatsuya Ishigaki, Kasumi Aoki, Hiroshi Noji, Keiichi Goshima, Ichiro Kobayashi, Hiroya Takamura and Yusuke Miyao, “Learning with Contrastive Examples for Data-to-text Generation”, The 28th International Conference on Computational Linguistics (COLING2020), December 8-11, 2020.
- Hiroaki Ozaki, Gaku Morio, Koreeda Yuta, Terufumi Morishita, Toshinori Miyoshi, “Hitachi at MRP 2020: Text-to-Graph-Notation Transducer”, The SIGNLL Conference on Computational Natural Language Learning, November 19-20, 2020.
- Shogo Murai, Hiroaki Mikami, Masanori Koyama, Shuji Suzuki, Takuya Akiba, “Online-Codistillation Meets LARS: Going beyond the Limit of Data Parallelism in Deep Learning”, The 5th Deep Learning on Supercomputers Workshop, Supercomputing Conference 2020, November 11, 2020.
- Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, Yutaka Satoh, “Pre-training without Natural Images”, Asian Conference on Computer Vision (ACCV), 2020.
- Nakamasa Inoue, Eisuke Yamagata, Hirokatsu Kataoka, “Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data”, International Conference on Pattern Recognition (ICPR), 2020.
- Yuchi Ishikawa, Seito Kasai, Yoshimitsu Aoki, Hirokatsu Kataoka, “Alleviating Over-segmentation Errors by Detecting Action Boundaries”, Winter Conference on Applications of Computer Vision (WACV), 2021.
- Yue Qiu, Yutaka Satoh, Ryota Suzuki, Kenji Iwata, Hirokatsu Kataoka, “Indoor Scene Change Captioning based on Multi-modality Data”, Sensors, 2020.
- Sol Cummings, Sho Nakamura, Yasunobu Shimazaki, “Building Change Detection Using Modified Siamese Neural Networks”, IEEE International Geoscience and Remote Sensing Symposium 2020 (IGARSS 2020), Sep. 2020.
- Terufumi Morishita, Gaku Morio, Hiroaki Ozaki, Toshinori Miyoshi, “Hitachi at SemEval-2020 Task 3: Exploring the Representation Spaces of Transformers for Human Sense Word Similarity”, International Workshop on Semantic Evaluation (SemEval) 2020, Dec. 2020.
- Terufumi Morishita, Gaku Morio, Hiroaki Ozaki, Toshinori Miyoshi, “Hitachi at SemEval-2020 Task 7: Stacking at Scale with Heterogeneous Language Models for Humour Recognition”, International Workshop on Semantic Evaluation (SemEval) 2020, Dec. 2020.
- Terufumi Morishita, Gaku Morio, Shota Horiguchi, Hiroaki Ozaki, Toshinori Miyoshi, “Hitachi at SemEval-2020 Task 8: Simpler but Effective Modality Ensemble for Meme Emotion Recognition”, International Workshop on Semantic Evaluation (SemEval) 2020, Dec. 2020.
- Gaku Morio, Terufumi Morishita, Hiroaki Ozaki, Toshinori Miyoshi, “Hitachi at SemEval-2020 Task 10: Emphasis Distribution Fusion on Fine-Tuned Language Models”, International Workshop on Semantic Evaluation (SemEval) 2020, Dec. 2020.
- Gaku Morio, Terufumi Morishita, Hiroaki Ozaki, Toshinori Miyoshi, “Hitachi at SemEval-2020 Task 11: An Empirical Study of Pre-Trained Transformer Family for Propaganda Detection”, International Workshop on Semantic Evaluation (SemEval) 2020, Dec. 2020.
- Sol Cummings, Sho Nakamura, Yasunobu Shimazaki, “Building Change Detection Using Modified Siamese Neural Networks”, IEEE International Geoscience and Remote Sensing Symposium 2020, Sep. 2020
- Hiroaki Akutsu, Akifumi Suzuki, Zhong Zhisheng, Kiyoharu Aizawa, “Ultra Low Bitrate Learned Image Compression by Selective Detail Decoding”, The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Jun. 2020
- Kazuki Uehara, “MULTI-SCALE EXPLAINABLE FEATURE LEARNING FOR PATHOLOGICAL IMAGE ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKS”, 27th IEEE International Conference on Image Processing (ICIP2020), Oct. 2020.
- Dario Bertero, Takeshi Homma, Kenichi Yokote, Makoto Iwayama, Kenji Nagamatsu, “Model Ensembling of ESIM and BERT for Dialogue Response Selection”, The Eighth Dialog System Technology Challenge (DSTC8) Workshop, Aug. 2020.
- Kohei Ozaki and Shuhei Yokoo, “1st place in Retrieval Challenge” and “3rd place in Recognition Challenge”, CVPR 2019 Second Landmark Recognition Workshop, 16 June 2019.
- Kohei Ozaki and Shuhei Yokoo, “Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset” (arXiv:1906.04087) [v1] Mon, 10 June 2019.
- Peng Chen, Mohamed Wahib, Shinichiro Takizawa, Ryousei Takano, Satoshi Matsuoka, “iFDK: A Scalable Framework for Instant High-resolution Image Reconstruction”, SC19, 17-22 November 2019.
- Peng Chen, Mohamed Wahib, Shinichiro Takizawa, Ryousei Takano, Satoshi Matsuoka, “A Versatile Software Systolic Execution Model for GPU Memory Bound Kernels”, SC19, 17-22 November 2019.
- Atsuro Okazawa, Tomoyuki Takahata, Tatsuya Harada, “Simultaneous transparent and non-transparent objects segmentation with multispectral scenes”, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 4-8 November 2019.
- Shinichiro Takizawa, “ABCI’s Scheduling Design for Accommodating Various AI Jobs”, 8th ADAC Workshop, Oct. 2019.
- Shinichiro Takizawa, “AI Bridging Cloud Infrastructure (ABCI) and its Communication Performance”, 7th Annual MVAPICH User Group (MUG) Meeting, Aug 2019
- Hirotaka Ogawa, “AI Bridging Cloud Infrastructure (ABCI) and its I/O architecture”, Fifth International Workshop on Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale, Jun 2019
学会発表
- 西野 涼子, 石垣 達也, 鷲野 壮平, 五十嵐 広希, 村井 昭彦, 鷲田 祐一, 永井 由佳里, “ホライゾン・スキャニングの自動化のための言語処理応用”, 言語処理 Vol.30 No.3, 2023年9月
- 室賀駿, 松本尚之, 小久保研, 岡崎俊也, 畠賢治, “材料分析の次世代DXへ向けたマルチモーダルAI技術”, プラスチック成形加工学会第34回年次大会, 2023年6月
- 室賀駿, “高分子材料の分析を加速するマルチモーダルＡＩ技術”, 23-1 NMR研究会「新たな機能の創成を目指した分子設計と特性解析」, 2023年5月
- 室賀駿, “マルチモーダルAI材料インフォマティクスによる複雑材料機能予測”, 応用物理学会春季講演会「プラズマ駆動科学とは何か～プラズマプロセスの新展開に期待して～」、2023年3月
- 室賀駿, “深層学習の複雑材料・プロセス開発への適用”, 化学工学会材料・界面部会共通基盤技術シンポジウム, 2023年1月
- 西脇一尊、大沼俊輔、門脇一真, “日本語情報抽出タスクのためのLayoutLMモデルの評価”, 言語処理学会第29回年次大会(NLP2023), 2023年3月
- 石川玲奈、森尚平、上田栞、デニス・カルコーフェン、斎藤英雄, “自由視点画像生成のためのフォーカルスタックによる多層シーン表現”, 第68回 複合現実感研究会, 2023年1月
- 龍田斉、吉田敬宏、水野裕介、横山広, “床版損傷写真画像の分析によるアルカリ骨材反応発生有無の関係づけに関する検討”, 土木学会令和4年度全国大会第77回年次学術講演会, 2022年9月
- 室賀駿、三木康彰、本田隆、森田裕史、岡崎俊也、畠賢治, “マルチモーダルAIによる高分子複合材料の新規インフォマティクス技術の開発”, 第31回ポリマー材料フォーラム, 2022年11月
- 室賀駿, “複雑・複合材料系へのAI技術の開拓”, 第12回CSJ化学フェスタ2022, 2022年10月
- 室賀駿, “データサイエンスとナノ材料の関わり”, 第59回炭素材料夏季セミナー, 2022年9月
- 室賀駿、三木康彰、本田隆、森田裕史、岡崎俊也、畠賢治, “マルチモーダルAIによる複雑・複合材料系のデータ駆動型技術の開発”, 化学工学会第53回秋季大会, 2022年9月
- 室賀駿、三木康彰、本田隆、森田裕史、岡崎俊也、畠賢治, “マルチモーダルAIによる複雑・複合材料系の仮想実験技術の開発”, 第83回応用物理学会秋季学術講演会, 2022年9月
- 一藁秀行、伊藤洋、山本健次郎、森裕紀、尾形哲也, “モダリティ注意による深層予測学習の解釈性とノイズロバスト性の向上”, ロボティクス・メカトロニクス 講演会 2022, 2022年6月
- 大山洋介，白幡晃一, ”大規模流体解析に向けたOpenFOAMの計算高速化”, オープンCAEシンポジウム2021, 2021年12月
- 谷村勇輔, “ABCIにおけるストレージサービスの紹介”. Gfarmシンポジウム2021, 2021年10月
- 石垣達也, トピチゴラン, 濵園侑美, 能地宏, 小林一郎, 宮尾祐介, 高村大也, ”レーシングゲーム実況生成”, 情報処理学会第250回自然言語処理研究会 (NL250) , 2021年9月
- 大洞日音, 戸次大介, “DTSの部分体系のための定理自動証明器の実装に向けて”, 言語処理学会第72回年次大会, 2021年3月
- 市村直幸，“空間周波数損失を用いた畳み込みニューラルネットワークの学習”，電子情報通信学会，パターン認識・メディア理解研究会（PRMU），2021年3月
- 河内祐太, 野里博和, 池田篤史, 坂無英徳, ”内視鏡画像における病変領域のあいまいな境界の学習手法”, 電子情報通信学会, PRMU2020-51, 2020年12月
- 上原由衣, 石垣達也, 青木花純, 能地 宏, 五島圭一, 小林一郎, 高村大也，宮尾 祐介, 「疑似負例を用いたdata-to-textモデルの学習」,情報処理学会自然言語処理研究会, 2020年12月
- 伊藤 亮, 「生シイタケの自動仕分け装置の開発」, 第３８回日本ロボット学会学術講演会, 2020年10月
- 上原由衣, 石垣達也, 青木花純, 能地 宏, 五島圭一, 小林一郎, 高村大也， 宮尾 祐介, 「疑似負例を用いたdata-to-textモデルの学習」, 情報処理学会自然言語処理研究会, 2020年12月
- 河村昂軌, 「GPUクラスタにおける動的負荷分散を用いた粒子法によるスロッシング計算」, 日本流体力学会 年会2020, 2020年9月
- 綱島秀樹 (早大, 産総研), 大川武彦 (東大), 相澤宏旭 (岐阜大), 片岡雄裕 (産総研), 森島繁生 (早大), 「Object-aware表現学習の安定化のためのKLダイバージェンスの周期性アニーリング」, MIRU2020, 2020年8月
- 龍田斉, 「橋梁管理カルテ情報から損傷原因および補修工法の推定におけるGBDTの活用(仮題)」, 土木学会 令和2年度全国大会 第75回年次学術講演会, 2020年9月
- 滝澤真一朗, 坂部昌久, 谷村勇輔, 小川宏高, “ABCI上でのジョブ実行履歴の分析による深層学習計算の傾向把握”, 第176回ハイパフォーマンスコンピューティング研究会, 2020年9月
- 河村昂軌, 「GPUクラスタを用いた粒子法によるLNGタンクのスロッシングとスワーリング計算」, 日本船舶海洋工学会 令和2年春季講演会, 2020年5月
- 中田秀基、他, 「エッジ、クラウド間分散処理に向けた動作識別手法の検討」, 電子情報通信学会 第１２回データ工学と情報マネージメントに関するフォーラム, 2020年3月
- 本田 巧, 笠置 明彦, 福本 尚人, 大辻 弘貴, 土肥 義康, 田原 司睦, 中島 耕太, “AI橋渡しクラウドABCIにおけるLinpack benchmarkの最適化と性能評価”, 情報処理学会 研究報告 ハイパフォーマンスコンピューティング(HPC), 2018-HPC-167, Dec. 2018.
- 谷村勇輔, 滝澤真一朗, 小川宏高, 浜西貴宏, “ABCIクラウドストレージサービスの構築と評価”, 第172回ハイパフォーマンスコンピューティング研究会, 2019年9月
- 滝澤真一朗. “ABCIのストレージサービスの現状と拡張計画”, Gfarmワークショップ2019, 2019年2月
- 佐藤仁, 溝手竜, 滝澤真一朗. “AI橋渡しクラウドABCIの性能評価”, 第166回ハイパフォーマンスコンピューティング研究会, 2018年9月
- 小川宏高, 松岡聡, 佐藤仁, 高野了成, 滝澤真一朗, 谷村勇輔, 三浦信一, 関口 智嗣, “世界最大規模のオープンAIインフラストラクチャ AI橋渡しクラウド（ABCI）の概要”, 第165回ハイパフォーマンスコンピューティング研究会, 2018年7月
- 小川宏高, 松岡聡, 佐藤仁, 高野了成, 滝澤真一朗, 谷村勇輔, 三浦信一, 関口智嗣, “AI橋渡しクラウド— AI Bridging Cloud Infrastructure (ABCI) — の構想”, 第160回ハイパフォーマンスコンピューティング研究会. 2017年7月
データ公開
- PALSAR散乱電力分解マップ： https://www.airc.aist.go.jp/gsrt/ ; 2020年4月
- PALSAR-2散乱電力分解マップ: https://www.airc.aist.go.jp/gsrt/ ; 2020年3月
ABCI参考文献
- Ryousei Takano, Shinichiro Takizawa, Yusuke Tanimura, Hidemoto Nakada, Hirotaka Ogawa, “ABCI 3.0: Evolution of the leading AI infrastructure in Japan”, arXiv:2411.09134 [cs.NI], November 2024.
- Shinichiro Takizawa, Yusuke Tanimura, Hidemoto Nakada, Ryousei Takano, Hirotaka Ogawa, “ABCI 2.0: Advances in Open AI Computing Infrastructure at AIST”, IPSJ SIG Technical Reports, 2021-HPC-180, July 2021.
ABCIのベンチマーク結果
