Search Results for author: Yewon Kim

Found 9 papers, 3 papers with code

Interactive Reinforcement Learning for Table Balancing Robot

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

SoTTA: Robust Test-Time Adaptation on Noisy Data Streams

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.

Autonomous Driving Test-time Adaptation

RPLKG: Robust Prompt Learning with Knowledge Graph

no code implementations21 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.

Domain Generalization Few-Shot Learning +1

Towards Explainable AI Writing Assistants for Non-native English Speakers

no code implementations5 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.

NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation

1 code implementation10 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.

Autonomous Driving Test-time Adaptation

MuSE-SVS: Multi-Singer Emotional Singing Voice Synthesizer that Controls Emotional Intensity

no code implementations2 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.

DAPPER: Label-Free Performance Estimation after Personalization for Heterogeneous Mobile Sensing

no code implementations22 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.

Domain Adaptation

Cannot find the paper you are looking for? You can Submit a new open access paper.