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pro vyhledávání: '"Hao, Junheng"'
Autor:
Abdin, Marah, Jacobs, Sam Ade, Awan, Ammar Ahmad, Aneja, Jyoti, Awadallah, Ahmed, Awadalla, Hany, Bach, Nguyen, Bahree, Amit, Bakhtiari, Arash, Bao, Jianmin, Behl, Harkirat, Benhaim, Alon, Bilenko, Misha, Bjorck, Johan, Bubeck, Sébastien, Cai, Qin, Cai, Martin, Mendes, Caio César Teodoro, Chen, Weizhu, Chaudhary, Vishrav, Chen, Dong, Chen, Dongdong, Chen, Yen-Chun, Chen, Yi-Ling, Chopra, Parul, Dai, Xiyang, Del Giorno, Allie, de Rosa, Gustavo, Dixon, Matthew, Eldan, Ronen, Fragoso, Victor, Iter, Dan, Gao, Mei, Gao, Min, Gao, Jianfeng, Garg, Amit, Goswami, Abhishek, Gunasekar, Suriya, Haider, Emman, Hao, Junheng, Hewett, Russell J., Huynh, Jamie, Javaheripi, Mojan, Jin, Xin, Kauffmann, Piero, Karampatziakis, Nikos, Kim, Dongwoo, Khademi, Mahoud, Kurilenko, Lev, Lee, James R., Lee, Yin Tat, Li, Yuanzhi, Li, Yunsheng, Liang, Chen, Liden, Lars, Liu, Ce, Liu, Mengchen, Liu, Weishung, Lin, Eric, Lin, Zeqi, Luo, Chong, Madan, Piyush, Mazzola, Matt, Mitra, Arindam, Modi, Hardik, Nguyen, Anh, Norick, Brandon, Patra, Barun, Perez-Becker, Daniel, Portet, Thomas, Pryzant, Reid, Qin, Heyang, Radmilac, Marko, Rosset, Corby, Roy, Sambudha, Ruwase, Olatunji, Saarikivi, Olli, Saied, Amin, Salim, Adil, Santacroce, Michael, Shah, Shital, Shang, Ning, Sharma, Hiteshi, Shukla, Swadheen, Song, Xia, Tanaka, Masahiro, Tupini, Andrea, Wang, Xin, Wang, Lijuan, Wang, Chunyu, Wang, Yu, Ward, Rachel, Wang, Guanhua, Witte, Philipp, Wu, Haiping, Wyatt, Michael, Xiao, Bin, Xu, Can, Xu, Jiahang, Xu, Weijian, Yadav, Sonali, Yang, Fan, Yang, Jianwei, Yang, Ziyi, Yang, Yifan, Yu, Donghan, Yuan, Lu, Zhang, Chengruidong, Zhang, Cyril, Zhang, Jianwen, Zhang, Li Lyna, Zhang, Yi, Zhang, Yue, Zhang, Yunan, Zhou, Xiren
We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi
Externí odkaz:
http://arxiv.org/abs/2404.14219
Autor:
Ma, Yubo, Gou, Zhibin, Hao, Junheng, Xu, Ruochen, Wang, Shuohang, Pan, Liangming, Yang, Yujiu, Cao, Yixin, Sun, Aixin, Awadalla, Hany, Chen, Weizhu
Scientific reasoning poses an excessive challenge for even the most advanced Large Language Models (LLMs). To make this task more practical and solvable for LLMs, we introduce a new task setting named tool-augmented scientific reasoning. This setting
Externí odkaz:
http://arxiv.org/abs/2402.11451
Autor:
Feng, Jiazhan, Xu, Ruochen, Hao, Junheng, Sharma, Hiteshi, Shen, Yelong, Zhao, Dongyan, Chen, Weizhu
Logical reasoning is a fundamental aspect of human intelligence and a key component of tasks like problem-solving and decision-making. Recent advancements have enabled Large Language Models (LLMs) to potentially exhibit reasoning capabilities, but co
Externí odkaz:
http://arxiv.org/abs/2311.06158
Pandemic(epidemic) modeling, aiming at disease spreading analysis, has always been a popular research topic especially following the outbreak of COVID-19 in 2019. Some representative models including SIR-based deep learning prediction models have sho
Externí odkaz:
http://arxiv.org/abs/2212.02575
Autor:
Zhang, Yu, Shen, Zhihong, Wu, Chieh-Han, Xie, Boya, Hao, Junheng, Wang, Ye-Yi, Wang, Kuansan, Han, Jiawei
Large-scale multi-label text classification (LMTC) aims to associate a document with its relevant labels from a large candidate set. Most existing LMTC approaches rely on massive human-annotated training data, which are often costly to obtain and suf
Externí odkaz:
http://arxiv.org/abs/2202.05932
Many large-scale knowledge bases simultaneously represent two views of knowledge graphs (KGs): an ontology view for abstract and commonsense concepts, and an instance view for specific entities that are instantiated from ontological concepts. Existin
Externí odkaz:
http://arxiv.org/abs/2103.08115
Publikováno v:
In Procs of the 11th ACM BCB, pp. 1-10. 2020
The widespread of Coronavirus has led to a worldwide pandemic with a high mortality rate. Currently, the knowledge accumulated from different studies about this virus is very limited. Leveraging a wide-range of biological knowledge, such as gene onto
Externí odkaz:
http://arxiv.org/abs/2103.04283
Publikováno v:
In Transportation Research Part B November 2018 117 Part A:101-116
Autor:
Hao, Junheng
Presentation slides forIncorporating Ontological Information in Knowledge Graph Learning and Empowered Applications Speaker: Junheng Hao (Microsoft)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::081d27708cf7652e64f4ef20153a1d5e
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