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pro vyhledávání: '"Nimishakavi, Madhav"'
Recently, pretraining methods for the Graph Neural Networks (GNNs) have been successful at learning effective representations from unlabeled graph data. However, most of these methods rely on pairwise relations in the graph and do not capture the und
Externí odkaz:
http://arxiv.org/abs/2311.11368
Autor:
Chandra, Shantanu, Mishra, Pushkar, Yannakoudakis, Helen, Nimishakavi, Madhav, Saeidi, Marzieh, Shutova, Ekaterina
Over the past few years, there has been a substantial effort towards automated detection of fake news on social media platforms. Existing research has modeled the structure, style, content, and patterns in dissemination of online posts, as well as th
Externí odkaz:
http://arxiv.org/abs/2008.06274
Autor:
Yadati, Naganand, Nimishakavi, Madhav, Yadav, Prateek, Nitin, Vikram, Louis, Anand, Talukdar, Partha
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise. Hypergraphs provide a flexible and natural modeling tool to model such complex relationships. The obv
Externí odkaz:
http://arxiv.org/abs/1809.02589
Autor:
Yadav, Prateek, Nimishakavi, Madhav, Yadati, Naganand, Vashishth, Shikhar, Rajkumar, Arun, Talukdar, Partha
Semi-supervised learning on graph structured data has received significant attention with the recent introduction of Graph Convolution Networks (GCN). While traditional methods have focused on optimizing a loss augmented with Laplacian regularization
Externí odkaz:
http://arxiv.org/abs/1805.11365
Low rank tensor completion is a well studied problem and has applications in various fields. However, in many real world applications the data is dynamic, i.e., new data arrives at different time intervals. As a result, the tensors used to represent
Externí odkaz:
http://arxiv.org/abs/1802.06371
One of the popular approaches for low-rank tensor completion is to use the latent trace norm regularization. However, most existing works in this direction learn a sparse combination of tensors. In this work, we fill this gap by proposing a variant o
Externí odkaz:
http://arxiv.org/abs/1712.01193
Autor:
Nimishakavi, Madhav, Talukdar, Partha
Relation Schema Induction (RSI) is the problem of identifying type signatures of arguments of relations from unlabeled text. Most of the previous work in this area have focused only on binary RSI, i.e., inducing only the subject and object type signa
Externí odkaz:
http://arxiv.org/abs/1707.01917
Given a set of documents from a specific domain (e.g., medical research journals), how do we automatically build a Knowledge Graph (KG) for that domain? Automatic identification of relations and their schemas, i.e., type signature of arguments of rel
Externí odkaz:
http://arxiv.org/abs/1605.04227