# CLOVA AI Lab @ AAAI 2022

## Paper presentation

#### \[Poster Session Time 1] Feb 25 @ 12:45am-2:30am (PST) /  Feb 25, Fri @ 5:45pm-7:30pm (KST)                                                                          

#### \[Poster Session Time 2] Feb 26 @ 4:45pm-6:30pm (PST) / Feb 27, Sun @ 9:45am-11:30am(KST)

Teakgyu Hong (NAVER CLOVA), Donghyun Kim (NAVER CLOVA), Mingi Ji (KAIST, NAVER CLOVA intern), Wonseok Hwang (LBox), Daehyun Nam (Upstage), Sungrae Park (Upstage). [BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents](https://arxiv.org/abs/2108.04539).

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#### \[Poster Session Time 1] Feb 25 @ 4:45pm-6:30pm (PST) / Feb 26, Sat @ 09:45am-11:30am (KST)     

#### \[Poster Session Time 2] Feb 27 @ 12:45am-2:30am (PST) / Feb 27, Sun @ 5:45pm-7:30pm (KST)

Minseok Kim (KAIST), Hwanjun Song (NAVER AI Lab), Yooju Shin (KAIST), Dongmin Park (KAIST), Kijung Shin (KAIST), Jae-Gil Lee (KAIST). [Meta-Learning for Online Update of Recommender Systems](https://minseokkim.net/publication/2022melon_aaai/2022melon_aaai.pdf). 

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#### \[Poster Session Time 1] Feb 25 @ 4:45pm-6:30pm (PST) / Feb 26, Sat @ 9:45am-11:30am (KST)

#### \[Poster Session Time 2] Feb 28 @ 12:45am-2:30am (PST)  / Feb 28, Mon @ 5:45pm-7:30pm(KST)

#### \[Oral Session Time] Feb 25 @ 6:30pm-7:45pm (PST) / Feb 26, Sat @ 11:30am-12:45pm (KST)

Doyoung Kim (KAIST, NAVER AI Lab Intern), Hyangsuk Min (KAIST), Youngeun Nam (KAIST), Hwanjun Song (NAVER AI Lab), Susik Yoon (KAIST), Minseok Kim (KAIST), Jae-Gil Lee (KAIST)**.** COVID-EENet: Predicting Fine-Grained Impact of COVID-19 on Local Economies. 

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#### \[Poster Session Time 1] Feb 25 @ 4:45pm-6:30pm (PST) / Feb 26, Sat @ 09:45am-11:30am (KST)

#### \[Poster Session Time 2] Feb 27 @ 8:45am-10:30am (PST) / Feb 28, Mon @ 1:45am-3:30am (KST)

Hazel Kim (Yonsei Uni, NAVER AI Lab intern), Daecheol Woo (Yonsei Uni), Seong Joon Oh (NAVER AI Lab), Jeong-Won Cha (Changwon National Univ), Yo-Sub Han (Yonsei Univ). [ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification](https://arxiv.org/abs/2112.11916). 


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