Zobrazeno 1 - 10
of 2 571
pro vyhledávání: '"Portet A"'
Autor:
Haider, Emman, Perez-Becker, Daniel, Portet, Thomas, Madan, Piyush, Garg, Amit, Ashfaq, Atabak, Majercak, David, Wen, Wen, Kim, Dongwoo, Yang, Ziyi, Zhang, Jianwen, Sharma, Hiteshi, Bullwinkel, Blake, Pouliot, Martin, Minnich, Amanda, Chawla, Shiven, Herrera, Solianna, Warreth, Shahed, Engler, Maggie, Lopez, Gary, Chikanov, Nina, Dheekonda, Raja Sekhar Rao, Jagdagdorj, Bolor-Erdene, Lutz, Roman, Lundeen, Richard, Westerhoff, Tori, Bryan, Pete, Seifert, Christian, Kumar, Ram Shankar Siva, Berkley, Andrew, Kessler, Alex
Recent innovations in language model training have demonstrated that it is possible to create highly performant models that are small enough to run on a smartphone. As these models are deployed in an increasing number of domains, it is critical to en
Externí odkaz:
http://arxiv.org/abs/2407.13833
Autor:
Fournier, Hippolyte, Alisamir, Sina, Azzakhnini, Safaa, Chainay, Hanna, Koenig, Olivier, Zsoldos, Isabella, Trân, Eléeonore, Bailly, Gérard, Elisei, Frédéeric, Bouchot, Béatrice, Varini, Brice, Constant, Patrick, Fruitet, Joan, Tarpin-Bernard, Franck, Rossato, Solange, Portet, François, Ringeval, Fabien
We present THERADIA WoZ, an ecological corpus designed for audiovisual research on affect in healthcare. Two groups of senior individuals, consisting of 52 healthy participants and 9 individuals with Mild Cognitive Impairment (MCI), performed Compute
Externí odkaz:
http://arxiv.org/abs/2405.06728
Autor:
Abdin, Marah, Aneja, Jyoti, Awadalla, Hany, Awadallah, Ahmed, Awan, Ammar Ahmad, Bach, Nguyen, Bahree, Amit, Bakhtiari, Arash, Bao, Jianmin, Behl, Harkirat, Benhaim, Alon, Bilenko, Misha, Bjorck, Johan, Bubeck, Sébastien, Cai, Martin, Cai, Qin, Chaudhary, Vishrav, Chen, Dong, Chen, Dongdong, Chen, Weizhu, Chen, Yen-Chun, Chen, Yi-Ling, Cheng, Hao, Chopra, Parul, Dai, Xiyang, Dixon, Matthew, Eldan, Ronen, Fragoso, Victor, Gao, Jianfeng, Gao, Mei, Gao, Min, Garg, Amit, Del Giorno, Allie, Goswami, Abhishek, Gunasekar, Suriya, Haider, Emman, Hao, Junheng, Hewett, Russell J., Hu, Wenxiang, Huynh, Jamie, Iter, Dan, Jacobs, Sam Ade, Javaheripi, Mojan, Jin, Xin, Karampatziakis, Nikos, Kauffmann, Piero, Khademi, Mahoud, Kim, Dongwoo, Kim, Young Jin, Kurilenko, Lev, Lee, James R., Lee, Yin Tat, Li, Yuanzhi, Li, Yunsheng, Liang, Chen, Liden, Lars, Lin, Xihui, Lin, Zeqi, Liu, Ce, Liu, Liyuan, Liu, Mengchen, Liu, Weishung, Liu, Xiaodong, Luo, Chong, Madan, Piyush, Mahmoudzadeh, Ali, Majercak, David, Mazzola, Matt, Mendes, Caio César Teodoro, Mitra, Arindam, Modi, Hardik, Nguyen, Anh, Norick, Brandon, Patra, Barun, Perez-Becker, Daniel, Portet, Thomas, Pryzant, Reid, Qin, Heyang, Radmilac, Marko, Ren, Liliang, de Rosa, Gustavo, Rosset, Corby, Roy, Sambudha, Ruwase, Olatunji, Saarikivi, Olli, Saied, Amin, Salim, Adil, Santacroce, Michael, Shah, Shital, Shang, Ning, Sharma, Hiteshi, Shen, Yelong, Shukla, Swadheen, Song, Xia, Tanaka, Masahiro, Tupini, Andrea, Vaddamanu, Praneetha, Wang, Chunyu, Wang, Guanhua, Wang, Lijuan, Wang, Shuohang, Wang, Xin, Wang, Yu, Ward, Rachel, Wen, Wen, Witte, Philipp, Wu, Haiping, Wu, Xiaoxia, Wyatt, Michael, Xiao, Bin, Xu, Can, Xu, Jiahang, Xu, Weijian, Xue, Jilong, Yadav, Sonali, Yang, Fan, Yang, Jianwei, Yang, Yifan, Yang, Ziyi, Yu, Donghan, Yuan, Lu, Zhang, Chenruidong, 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:
Ek, Sannara, Presotto, Riccardo, Civitarese, Gabriele, Portet, François, Lalanda, Philippe, Bettini, Claudio
Human Activity Recognition (HAR) based on the sensors of mobile/wearable devices aims to detect the physical activities performed by humans in their daily lives. Although supervised learning methods are the most effective in this task, their effectiv
Externí odkaz:
http://arxiv.org/abs/2404.15331
Autor:
Kocabiyikoglu, Ali Can, Portet, François, Babouchkine, Jean-Marc, Gibert, Prudence, Blanchon, Hervé, Gavazzi, Gaëtan
Hospital information systems (HIS) have become an essential part of healthcare institutions and now incorporate prescribing support software. Prescription support software allows for structured information capture, which improves the safety, appropri
Externí odkaz:
http://arxiv.org/abs/2311.03510
This study explores the capabilities of prompt-driven Large Language Models (LLMs) like ChatGPT and GPT-4 in adhering to human guidelines for dialogue summarization. Experiments employed DialogSum (English social conversations) and DECODA (French cal
Externí odkaz:
http://arxiv.org/abs/2310.16810
Quotation extraction is a widely useful task both from a sociological and from a Natural Language Processing perspective. However, very little data is available to study this task in languages other than English. In this paper, we present a manually
Externí odkaz:
http://arxiv.org/abs/2309.10604
Autor:
Parcollet, Titouan, Nguyen, Ha, Evain, Solene, Boito, Marcely Zanon, Pupier, Adrien, Mdhaffar, Salima, Le, Hang, Alisamir, Sina, Tomashenko, Natalia, Dinarelli, Marco, Zhang, Shucong, Allauzen, Alexandre, Coavoux, Maximin, Esteve, Yannick, Rouvier, Mickael, Goulian, Jerome, Lecouteux, Benjamin, Portet, Francois, Rossato, Solange, Ringeval, Fabien, Schwab, Didier, Besacier, Laurent
Self-supervised learning (SSL) is at the origin of unprecedented improvements in many different domains including computer vision and natural language processing. Speech processing drastically benefitted from SSL as most of the current domain-related
Externí odkaz:
http://arxiv.org/abs/2309.05472
Automatic dialogue summarization is a well-established task with the goal of distilling the most crucial information from human conversations into concise textual summaries. However, most existing research has predominantly focused on summarizing fac
Externí odkaz:
http://arxiv.org/abs/2307.12371
Autor:
Presotto, Riccardo, Ek, Sannara, Civitarese, Gabriele, Portet, François, Lalanda, Philippe, Bettini, Claudio
The use of supervised learning for Human Activity Recognition (HAR) on mobile devices leads to strong classification performances. Such an approach, however, requires large amounts of labeled data, both for the initial training of the models and for
Externí odkaz:
http://arxiv.org/abs/2306.13735