Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Sahrawat, Dhruva"'
Autor:
Sahrawat, Dhruva, Kumar, Yaman, Aggarwal, Shashwat, Yin, Yifang, Shah, Rajiv Ratn, Zimmermann, Roger
Speech as a natural signal is composed of three parts - visemes (visual part of speech), phonemes (spoken part of speech), and language (the imposed structure). However, video as a medium for the delivery of speech and a multimedia construct has most
Externí odkaz:
http://arxiv.org/abs/2006.08599
Developing nations lack adequate number of hospitals with modern equipment and skilled doctors. Hence, a significant proportion of these nations' population, particularly in rural areas, is not able to avail specialized and timely healthcare faciliti
Externí odkaz:
http://arxiv.org/abs/2003.03295
Autor:
Sahrawat, Dhruva, Mahata, Debanjan, Kulkarni, Mayank, Zhang, Haimin, Gosangi, Rakesh, Stent, Amanda, Sharma, Agniv, Kumar, Yaman, Shah, Rajiv Ratn, Zimmermann, Roger
In this paper, we formulate keyphrase extraction from scholarly articles as a sequence labeling task solved using a BiLSTM-CRF, where the words in the input text are represented using deep contextualized embeddings. We evaluate the proposed architect
Externí odkaz:
http://arxiv.org/abs/1910.08840
Autor:
Kumar, Yaman, Sahrawat, Dhruva, Maheshwari, Shubham, Mahata, Debanjan, Stent, Amanda, Yin, Yifang, Shah, Rajiv Ratn, Zimmermann, Roger
Visual Speech Recognition (VSR) is the process of recognizing or interpreting speech by watching the lip movements of the speaker. Recent machine learning based approaches model VSR as a classification problem; however, the scarcity of training data
Externí odkaz:
http://arxiv.org/abs/1901.10139
Autor:
Sahrawat, Dhruva, Mahata, Debanjan, Zhang, Haimin, Kulkarni, Mayank, Sharma, Agniv, Gosangi, Rakesh, Stent, Amanda, Kumar, Yaman, Shah, Rajiv Ratn, Zimmermann, Roger
Publikováno v:
Advances in Information Retrieval
In this paper, we formulate keyphrase extraction from scholarly articles as a sequence labeling task solved using a BiLSTM-CRF, where the words in the input text are represented using deep contextualized embeddings. We evaluate the proposed architect
Autor:
Yin, Yifang, Sunderrajan, Abhinav, Huang, Xiaocheng, Varadarajan, Jagannadan, Wang, Guanfeng, Sahrawat, Dhruva, Zhang, Ying, Zimmermann, Roger, Ng, See-Kiong
Publikováno v:
21st SIGSPATIAL International Conference on Advances in Geographic Information Systems; 11/5/2019, p36-39, 4p