Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Chatterjee, Niladri"'
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
Autor:
Sikdar, Abhinava, Chatterjee, Niladri
Normalization of SMS text, commonly known as texting language, is being pursued for more than a decade. A probabilistic approach based on the Trie data structure was proposed in literature which was found to be better performing than HMM based approa
Externí odkaz:
http://arxiv.org/abs/2008.01297
Autor:
Yadav, Nidhika, Chatterjee, Niladri
Most problems in Machine Learning cater to classification and the objects of universe are classified to a relevant class. Ranking of classified objects of universe per decision class is a challenging problem. We in this paper propose a novel Rough Se
Externí odkaz:
http://arxiv.org/abs/2002.03259