Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Chatterjee, Niladri"'
Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each hold data from a different distribution, which can yield to catastrophi
Externí odkaz:
http://arxiv.org/abs/2211.00184
Autor:
Chen, Qingyu, Allot, Alexis, Leaman, Robert, Doğan, Rezarta Islamaj, Du, Jingcheng, Fang, Li, Wang, Kai, Xu, Shuo, Zhang, Yuefu, Bagherzadeh, Parsa, Bergler, Sabine, Bhatnagar, Aakash, Bhavsar, Nidhir, Chang, Yung-Chun, Lin, Sheng-Jie, Tang, Wentai, Zhang, Hongtong, Tavchioski, Ilija, Pollak, Senja, Tian, Shubo, Zhang, Jinfeng, Otmakhova, Yulia, Yepes, Antonio Jimeno, Dong, Hang, Wu, Honghan, Dufour, Richard, Labrak, Yanis, Chatterjee, Niladri, Tandon, Kushagri, Laleye, Fréjus, Rakotoson, Loïc, Chersoni, Emmanuele, Gu, Jinghang, Friedrich, Annemarie, Pujari, Subhash Chandra, Chizhikova, Mariia, Sivadasan, Naveen, Lu, Zhiyong
The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design vaccines and drugs. The related findings have been reported in biomed
Externí odkaz:
http://arxiv.org/abs/2204.09781
An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare. Despite their superior performances, many models are black boxes in nature which are hard to explain. There are growing eff
Externí odkaz:
http://arxiv.org/abs/2203.17081
Autor:
Singh, Yashank, Chatterjee, Niladri
The present paper explores a novel variant of Random Indexing (RI) based representations for encoding language data with a view to using them in a dynamic scenario where events are happening in a continuous fashion. As the size of the representations
Externí odkaz:
http://arxiv.org/abs/2008.12552