P
Prateek Yadav
Researcher at LinkedIn
Publications - 21
Citations - 469
Prateek Yadav is an academic researcher from LinkedIn. The author has contributed to research in topics: Graph (abstract data type) & Computer science. The author has an hindex of 8, co-authored 16 publications receiving 258 citations. Previous affiliations of Prateek Yadav include Indian Institute of Science.
Papers
More filters
Posted Content
HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
Naganand Yadati,Madhav Nimishakavi,Prateek Yadav,Vikram Nitin,Anand Louis,Partha Pratim Talukdar +5 more
TL;DR: HyperGCN as mentioned in this paper is a graph convolutional network (GCN) for hypergraph-based semi-supervised learning (SSL) where the goal is to assign labels to initially unlabeled vertices in a hypergraph.
Proceedings Article
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs
Naganand Yadati,Madhav Nimishakavi,Prateek Yadav,Vikram Nitin,Anand Louis,Partha Pratim Talukdar +5 more
TL;DR: This work proposes HyperGCN, a novel GCN for SSL on attributed hypergraphs, and shows how it can be used as a learning-based approach for combinatorial optimisation on NP-hard hypergraph problems.
Proceedings ArticleDOI
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
Shikhar Vashishth,Manik Bhandari,Prateek Yadav,Piyush Rai,Chiranjib Bhattacharyya,Partha Pratim Talukdar +5 more
TL;DR: This article proposed SynGCN, a flexible Graph Convolution based method for word embeddings, which utilizes the dependency context of a word without increasing the vocabulary size and outperformed existing methods on various intrinsic and extrinsic tasks and provided an advantage when used with ELMo.
Posted Content
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
Shikhar Vashishth,Manik Bhandari,Prateek Yadav,Piyush Rai,Chiranjib Bhattacharyya,Partha Pratim Talukdar +5 more
TL;DR: Word embeddings learned by SynGCN outperform existing methods on various intrinsic and extrinsic tasks and provide an advantage when used with ELMo and an effective framework for incorporating diverse semantic knowledge for further enhancing learned word representations are proposed.
Proceedings ArticleDOI
NHP: Neural Hypergraph Link Prediction
Naganand Yadati,Vikram Nitin,Madhav Nimishakavi,Prateek Yadav,Anand Louis,Partha Pratim Talukdar +5 more
TL;DR: This paper proposes Neural Hyperlink Predictor (NHP), a deep learning-based approach for link prediction over directed hypergraphs that adapts Graph Convolutional Network (GCN) and proposes two variants of NHP --NHP-U and NHP-D -- for link Prediction over undirected and directedhypergraphs, respectively.