no code implementations • ACL (splurobonlp) 2021 • Haein Jeon, Yewon Kim, Bo-Yeong Kang
With the development of robotics, the use of robots in daily life is increasing, which has led to the need for anyone to easily train robots to improve robot use.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 21 Mar 2024 • Mina Lee, Katy Ilonka Gero, John Joon Young Chung, Simon Buckingham Shum, Vipul Raheja, Hua Shen, Subhashini Venugopalan, Thiemo Wambsganss, David Zhou, Emad A. Alghamdi, Tal August, Avinash Bhat, Madiha Zahrah Choksi, Senjuti Dutta, Jin L. C. Guo, Md Naimul Hoque, Yewon Kim, Simon Knight, Seyed Parsa Neshaei, Agnia Sergeyuk, Antonette Shibani, Disha Shrivastava, Lila Shroff, Jessi Stark, Sarah Sterman, Sitong Wang, Antoine Bosselut, Daniel Buschek, Joseph Chee Chang, Sherol Chen, Max Kreminski, Joonsuk Park, Roy Pea, Eugenia H. Rho, Shannon Zejiang Shen, Pao Siangliulue
In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities.
1 code implementation • NeurIPS 2023 • Taesik Gong, Yewon Kim, Taeckyung Lee, Sorn Chottananurak, Sung-Ju Lee
To address this problem, we present Screening-out Test-Time Adaptation (SoTTA), a novel TTA algorithm that is robust to noisy samples.
no code implementations • 21 Apr 2023 • Yewon Kim, Yongtaek Lim, Dokyung Yoon, Kyungwoo Song
To improve the generalization performance on few-shot learning, there have been diverse efforts, such as prompt learning and adapter.
no code implementations • 5 Apr 2023 • Yewon Kim, Mina Lee, Donghwi Kim, Sung-Ju Lee
We highlight the challenges faced by non-native speakers when using AI writing assistants to paraphrase text.
1 code implementation • 10 Aug 2022 • Taesik Gong, Jongheon Jeong, Taewon Kim, Yewon Kim, Jinwoo Shin, Sung-Ju Lee
Test-time adaptation (TTA) is an emerging paradigm that addresses distributional shifts between training and testing phases without additional data acquisition or labeling cost; only unlabeled test data streams are used for continual model adaptation.
1 code implementation • ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2022 • Changdae Oh, Heeji Won, Junhyuk So, Taero Kim, Yewon Kim, Hosik Choi, Kyungwoo Song
We provide a new type of contrastive loss motivated by Gaussian and Student-t kernels for distributional contrastive learning with theoretical analysis.
no code implementations • 2 Mar 2022 • Sungjae Kim, Yewon Kim, Jewoo Jun, Injung Kim
We propose a multi-singer emotional singing voice synthesizer, Muse-SVS, that expresses emotion at various intensity levels by controlling subtle changes in pitch, energy, and phoneme duration while accurately following the score.
no code implementations • 22 Nov 2021 • Taesik Gong, Yewon Kim, Adiba Orzikulova, Yunxin Liu, Sung Ju Hwang, Jinwoo Shin, Sung-Ju Lee
However, various factors such as different users, devices, and environments impact the performance of such applications, thus making the domain shift (i. e., distributional shift between the training domain and the target domain) a critical issue in mobile sensing.