Zobrazeno 1 - 10
of 1 767
pro vyhledávání: '"Pham, Hai"'
Autor:
Padlewski, Piotr, Bain, Max, Henderson, Matthew, Zhu, Zhongkai, Relan, Nishant, Pham, Hai, Ong, Donovan, Aleksiev, Kaloyan, Ormazabal, Aitor, Phua, Samuel, Yeo, Ethan, Lamprecht, Eugenie, Liu, Qi, Wang, Yuqi, Chen, Eric, Fu, Deyu, Li, Lei, Zheng, Che, d'Autume, Cyprien de Masson, Yogatama, Dani, Artetxe, Mikel, Tay, Yi
We introduce Vibe-Eval: a new open benchmark and framework for evaluating multimodal chat models. Vibe-Eval consists of 269 visual understanding prompts, including 100 of hard difficulty, complete with gold-standard responses authored by experts. Vib
Externí odkaz:
http://arxiv.org/abs/2405.02287
Autor:
Reka Team, Ormazabal, Aitor, Zheng, Che, d'Autume, Cyprien de Masson, Yogatama, Dani, Fu, Deyu, Ong, Donovan, Chen, Eric, Lamprecht, Eugenie, Pham, Hai, Ong, Isaac, Aleksiev, Kaloyan, Li, Lei, Henderson, Matthew, Bain, Max, Artetxe, Mikel, Relan, Nishant, Padlewski, Piotr, Liu, Qi, Chen, Ren, Phua, Samuel, Yang, Yazheng, Tay, Yi, Wang, Yuqi, Zhu, Zhongkai, Xie, Zhihui
We introduce Reka Core, Flash, and Edge, a series of powerful multimodal language models trained from scratch by Reka. Reka models are able to process and reason with text, images, video, and audio inputs. This technical report discusses details of t
Externí odkaz:
http://arxiv.org/abs/2404.12387
Autor:
Do, Binh N, Tran, Tien V, Phan, Dung T, Nguyen, Hoang C, Nguyen, Thao T P, Nguyen, Huu C, Ha, Tung H, Dao, Hung K, Trinh, Manh V, Do, Thinh V, Nguyen, Hung Q, Vo, Tam T, Nguyen, Nhan P T, Tran, Cuong Q, Tran, Khanh V, Duong, Trang T, Pham, Hai X, Nguyen, Lam V, Nguyen, Kien T, Chang, Peter W S, Duong, Tuyen Van
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 11, p e22894 (2020)
BackgroundThe COVID-19 pandemic has imposed a heavy burden on health care systems and governments. Health literacy (HL) and eHealth literacy (as measured by the eHealth Literacy Scale [eHEALS]) are recognized as strategic public health elements but t
Externí odkaz:
https://doaj.org/article/44e52ec8321b4113977a6860577da930
Autor:
Pham, Hai X., Hadji, Isma, Xu, Xinnuo, Degutyte, Ziedune, Rainey, Jay, Kazakos, Evangelos, Fazly, Afsaneh, Tzimiropoulos, Georgios, Martinez, Brais
In this paper, we focus on task-specific question answering (QA). To this end, we introduce a method for generating exhaustive and high-quality training data, which allows us to train compact (e.g., run on a mobile device), task-specific QA models th
Externí odkaz:
http://arxiv.org/abs/2401.13594
Autor:
Tran, Bach Xuan, Nghiem, Son, Sahin, Oz, Vu, Tuan Manh, Ha, Giang Hai, Vu, Giang Thu, Pham, Hai Quang, Do, Hoa Thi, Latkin, Carl A, Tam, Wilson, Ho, Cyrus S H, Ho, Roger C M
Publikováno v:
Journal of Medical Internet Research, Vol 21, Iss 11, p e15511 (2019)
BackgroundArtificial intelligence (AI)–based technologies develop rapidly and have myriad applications in medicine and health care. However, there is a lack of comprehensive reporting on the productivity, workflow, topics, and research landscape of
Externí odkaz:
https://doaj.org/article/cc9548d318a246e9838b35c306ccb9f1
Autor:
Pham, Hai Ly, Kanjilal, Ankita
Repairing is one of the alternative business alternatives at the end-of-life phase of clothing, that is seen to be a potential solution for the biggest challenge that the fashion industry faces today - waste. The purpose of this research is to extend
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-29684
Autor:
Pham, Hai, Kim, Young Jin, Mukherjee, Subhabrata, Woodruff, David P., Poczos, Barnabas, Awadalla, Hany Hassan
Mixture-of-experts (MoE) architecture has been proven a powerful method for diverse tasks in training deep models in many applications. However, current MoE implementations are task agnostic, treating all tokens from different tasks in the same manne
Externí odkaz:
http://arxiv.org/abs/2308.15772
Due to the prohibitively high cost of creating error correction datasets, most Factual Claim Correction methods rely on a powerful verification model to guide the correction process. This leads to a significant drop in performance in domains like sci
Externí odkaz:
http://arxiv.org/abs/2305.14707
Despite their popularity in deep learning and machine learning in general, the theoretical properties of adaptive optimizers such as Adagrad, RMSProp, Adam or AdamW are not yet fully understood. In this paper, we develop a novel framework to study th
Externí odkaz:
http://arxiv.org/abs/2211.03970
Autor:
Dvornik, Nikita, Hadji, Isma, Pham, Hai, Bhatt, Dhaivat, Martinez, Brais, Fazly, Afsaneh, Jepson, Allan D.
Publikováno v:
ECCV 2022
In this work, we consider the problem of weakly-supervised multi-step localization in instructional videos. An established approach to this problem is to rely on a given list of steps. However, in reality, there is often more than one way to execute
Externí odkaz:
http://arxiv.org/abs/2210.04996