日本語
当研究室の論文がACLのWorkshopでBest Paper Awardを受賞しました
当研究室沖村さん，マシェルさん，河野さんの論文が現在開催されているACLのWorkshopでBest Paper Awardを受賞しました。
Our paper for IPSJ journal was accepted.
◼︎書誌情報 タイトル：Fixing the train-test objective discrepancy: Iterative Image Inpainting for Unsupervised Anomaly Detection 著 者：Hitoshi Nakanishi, Masahiro Suzuki, Yutaka Matuo 掲載号：J-Stage in August Vol.30 ◼︎概要 Autoencoders have emerged as popular methods for unsupervised anomaly detection, but they still have difficulty detecting local anomalies in real-world images due to lack of modeling small details. We have assessed…
Our paper was accepted for IJCAI-ECAI 2022 (Short).
Our paper was accepted for IJCAI-ECAI 2022 (Short). ■書誌情報 Takeshi Kojima, Yusuke Iwasawa, and Yutaka Matsuo. “Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment”, the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022) ■概要 Vision Transformer (ViT) is becoming more popular in…
Our paper was accepted for NAACL 2022 (main).
Our paper was accepted for NAACL 2022 (main). ◼︎書誌情報 David Ifeoluwa Adelani, Jesujoba Oluwadara Alabi, Angela Fan, Julia Kreutzer, Xiaoyu Shen, Machel Reid, Dana Ruiter, Dietrich Klakow, Peter Nabende, Ernie Chang, Tajuddeen Gwadabe, Freshia Sackey, Bonaventure F. P. Dossou, Chris Chinenye Emezue, Colin Leong, Michael Beukman, Shamsuddeen Hassan Muhammad, Guyo Dub Jarso, Oreen Yousuf, Andre…
Our paper was accepted for NAACL 2022 (main).
Our paper was accepted for NAACL 2022 (main). ◼︎書誌情報 Machel Reid and Mikel Artetxe “PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining”. The 2022 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2022). July 2022. Association for Computational Linguistics. ◼︎概要 Despite the success of multilingual sequence-to-sequence pretraining, most existing approaches…
Our paper was accepted for NeurIPS2022 (Spotlight)
Our paper was accepted for presentation at NeurIPS2022 (Spotlight) . ◼︎書誌情報 Hiroki Furuta, Yutaka Matsuo, Shixiang Shane Gu. “Generalized Decision Transformer for Offline Hindsight Information Matching”, International Conference on Learning Representations (ICLR2022). ◼︎概要 How to extract as much learning signal from each trajectory data has been a key problem in reinforcement learning (RL), where sample…
当研究室の論文が人工知能学会論文誌に採録されました
◼Information タイトル：Transformerと自己教師あり学習を用いたシーン解釈手法の提案 著 者： 小林 由弥 鈴木 雅大 松尾 豊 掲載号：第37巻2号 J-STAGE ◼Overview Ability to understand surrounding environment based on its components, namely objects, is one of the most important cognitive ability for intelligent agents. Human beings are able to decompose sensory input, i.e. visual stimulation, into some components based on its meaning or relationships between…
Our paper was accepted for Web Intelligence (Spotlight)
Our paper was accepted for Web Intelligence (Spotlight) 書誌情報 Hiromi Nakagawa, Yusuke Iwasawa, Yutaka Matsuo. “Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network.” Web Intelligence. Vol. XX. No. X. IOS Press, 2021. 概要 Recent advancements in computer-assisted learning systems have caused an increase in the research in knowledge tracing, wherein student performance is predicted…
Our paper was accepted for NeurIPS2021 (Spotlight)
Our paper was accepted for presentation at NeurIPS2021 (Spotlight) . ︎書誌情報 Yusuke Iwasawa, Yutaka Matsuo. “Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization”, Advances in Neural Information Processing Systems 2021 (NeurIPS2021). ︎概要 This paper presents a new algorithm for domain generalization (DG), test-time template adjuster (T3A), aiming to develop a model that performs well under conditions…
Our paper was accepted for NeurIPS2021
Our paper was accepted for presentation at NeurIPS2021 . ︎書誌情報 Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima, Yutaka Matsuo, and Shixiang Shane Gu. “Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning”, Advances in Neural Information Processing Systems 2021 (NeurIPS2021). ︎概要 Recently many algorithms were devised for reinforcement learning (RL) with function approximation. While…