1 code implementation • COLING 2022 • Dongsuk Oh, Yejin Kim, Hodong Lee, H. Howie Huang, Heuiseok Lim
Recent pre-trained language models (PLMs) achieved great success on many natural language processing tasks through learning linguistic features and contextualized sentence representation.
no code implementations • 27 Mar 2024 • Youngbin Lee, Yejin Kim, YongJae lee
Hence, the tricky point in stock recommendation is that recommendations should give good investment performance but also should not ignore individual preferences.
no code implementations • 27 Mar 2024 • Minjoo Choi, Seonmi Kim, Yejin Kim, Youngbin Lee, Joohwan Hong, YongJae lee
Recommender systems have been actively studied and applied in various domains to deal with information overload.
no code implementations • 27 Mar 2024 • Yejin Kim, Youngbin Lee, Minyoung Choe, Sungju Oh, YongJae lee
This paper explores the utilization of Temporal Graph Networks (TGN) for financial anomaly detection, a pressing need in the era of fintech and digitized financial transactions.
no code implementations • 27 Mar 2024 • Yejin Kim, Scott Rome, Kevin Foley, Mayur Nankani, Rimon Melamed, Javier Morales, Abhay Yadav, Maria Peifer, Sardar Hamidian, H. Howie Huang
It is essential to provide recommendations that are both personalized and diverse, rather than solely relying on achieving high rank-based performance, such as Click-through Rate, Recall, etc.
no code implementations • 24 Mar 2024 • Yejin Kim, Youngbin Lee, Vincent Yuan, Annika Lee, YongJae lee
Recommender systems, crucial for user engagement on platforms like e-commerce and streaming services, often lag behind users' evolving preferences due to static data reliance.
no code implementations • 21 Mar 2024 • Sehee Lim, Yejin Kim, Chi-Hyun Choi, Jy-yong Sohn, Byung-Hoon Kim
Improving the accessibility of psychotherapy with the aid of Large Language Models (LLMs) is garnering a significant attention in recent years.
no code implementations • 27 Feb 2024 • Disha Makhija, Joydeep Ghosh, Yejin Kim
To overcome this obstacle, in this work, we propose a novel framework for collaborative learning of HTE estimators across institutions via Federated Learning.
no code implementations • 11 Dec 2023 • Ruihan Yang, Yejin Kim, Aniruddha Kembhavi, Xiaolong Wang, Kiana Ehsani
Recent advancements in robotics have enabled robots to navigate complex scenes or manipulate diverse objects independently.
no code implementations • 5 Dec 2023 • Kiana Ehsani, Tanmay Gupta, Rose Hendrix, Jordi Salvador, Luca Weihs, Kuo-Hao Zeng, Kunal Pratap Singh, Yejin Kim, Winson Han, Alvaro Herrasti, Ranjay Krishna, Dustin Schwenk, Eli VanderBilt, Aniruddha Kembhavi
Reinforcement learning (RL) with dense rewards and imitation learning (IL) with human-generated trajectories are the most widely used approaches for training modern embodied agents.
1 code implementation • 13 Nov 2023 • Rimon Melamed, Lucas H. McCabe, Tanay Wakhare, Yejin Kim, H. Howie Huang, Enric Boix-Adsera
We discover that many natural-language prompts can be replaced by corresponding prompts that are unintelligible to humans but that provably elicit similar behavior in language models.
no code implementations • 13 Jun 2023 • Seonmi Kim, Youngbin Lee, Yejin Kim, Joohwan Hong, YongJae lee
Recommender systems have become essential tools for enhancing user experiences across various domains.
no code implementations • 18 Apr 2023 • TianHao Li, Sandesh Shetty, Advaith Kamath, Ajay Jaiswal, Xianqian Jiang, Ying Ding, Yejin Kim
Large pre-trained language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data.
1 code implementation • 13 Sep 2022 • Dongsuk Oh, Yejin Kim, Hodong Lee, H. Howie Huang, Heuiseok Lim
Recent pre-trained language models (PLMs) achieved great success on many natural language processing tasks through learning linguistic features and contextualized sentence representation.
no code implementations • 15 Oct 2021 • Pulakesh Upadhyaya, Kai Zhang, Can Li, Xiaoqian Jiang, Yejin Kim
Causal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care.
no code implementations • 27 Sep 2021 • Yaobin Ling, Pulakesh Upadhyaya, Luyao Chen, Xiaoqian Jiang, Yejin Kim
We also expect to provide the feasibility of HTE for personalized drug effectiveness.
1 code implementation • 4 Jul 2021 • Yan Ding, Xiaoqian Jiang, Yejin Kim
The RGCN model achieved an overall accuracy of 0. 872, an AUROC of 0. 919 and an AUPRC of 0. 838 for the testing dataset with the drug-protein interactions and the Mordred descriptors as the input.
no code implementations • 8 Dec 2020 • Yejin Kim, Manri Cheon, Junwoo Lee
Texture Transform Attention is used to create a new reassembled texture map using fine textures and coarse semantics that can efficiently transfer texture information as a result.
no code implementations • 23 Sep 2020 • Kanglin Hsieh, Yinyin Wang, Luyao Chen, Zhongming Zhao, Sean Savitz, Xiaoqian Jiang, Jing Tang, Yejin Kim
In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment.
no code implementations • 14 May 2019 • Xiaoqian Jiang, Samden Lhatoo, Guo-Qiang Zhang, Luyao Chen, Yejin Kim
Existing studies consider Alzheimer's disease (AD) a comorbidity of epilepsy, but also recognize epilepsy to occur more frequently in patients with AD than those without.
no code implementations • 14 May 2019 • Rui Zhang, Luca Giancardo, Danilo A. Pena, Yejin Kim, Hanghang Tong, Xiaoqian Jiang
In this paper, we studied the association between the change of structural brain volumes to the potential development of Alzheimer's disease (AD).
no code implementations • 14 May 2019 • Yejin Kim, Xiaoqian Jiang, Luyao Chen, Xiaojin Li, Licong Cui
Sleep change is commonly reported in Alzheimer's disease (AD) patients and their brain wave studies show decrease in dreaming and non-dreaming stages.
no code implementations • 11 Apr 2017 • Yejin Kim, Jimeng Sun, Hwanjo Yu, Xiaoqian Jiang
In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data.