当研究室の論文がACL2022のWorkshop(Insights from Negative Results in NLP)に採録されました。

当研究室の論文がACL2022のWorkshop(Insights from Negative Results in NLP)に採録されました。 ■書誌情報 Itsuki Okimura, Machel Reid, Makoto Kawano and Yutaka Matsuo, On the Impact of Data Augmentation on Downstream Performance in Natural Language Processing, the Third Workshop on Insights from Negative Results in NLP, ACL 2022, May 2022 ■概要 With in the broader scope of machine learning, data augmentation is…

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 Machine Learning.(Springer)

◼︎Information Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo. “Information-theoretic regularization for learning global features by sequential VAE”, Mach Learn (2021). https://doi.org/10.1007/s10994-021-06032-4 ◼︎Overview Sequential variational autoencoders (VAEs) with a global latent variable z have been studied for disentangling the global features of data, which is useful for several downstream tasks. To further assist the sequential VAEs in…

Our paper was accepted for UAI2021.

◼︎Information Akiyoshi Sannai, Masaaki Imaizumi, Makoto Kawano. “Improved Generalization Bounds of Group Invariant / Equivariant Deep Networks via Quotient Feature Spaces”, 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021). ◼︎Overview Numerous invariant (or equivariant) neural networks have succeeded in handling the invariant data such as point clouds and graphs. However, a generalization theory for…