Talukdar.net Website Review

department of computational and data sciences (cds) and department of computer science and automation (csa) it has an api and hadoop implementations of three graph-based semi-supervised learning algorithms. i am broadly interested in machine learning, natural language processing, data integration, and cognitive science. my recent research has focused on graph-based learning algorithms for large-scale information extraction and data integration, temporal information processing, automatic knowledge harvesting from large data, and neuro-semantics. icml (2017), ijcai (2016), kdd (2017), naacl (2018), nips (2018), wsdm (2018), www (2017). jan-may 2016: se 294: data an*lysis and visualization aug-dec 2015: e1 246: natural language understanding. confidence-based graph convolutional networks for semi-supervised learning. prateek yadav, madhav nimishakavi, naganand yadati, arun rajkumar and partha talukdar swayambhu nath ray, shib sankar dasgupta and partha talukdar international conference on empirical methods in natural language reside: improving distantly-supervised neural relation extraction using side information shikhar vashishth, rishabh joshi, sai suman prayaga, chiranjib bhattacharyya and partha talukdar mt-cgcnn: integrating crystal graph convolutional neural network with multitask learning for material property prediction cesi: canonicalizing open knowledge bases using embeddings and side information tom m. mitchell, w. cohen, e. hruschka, p. talukdar, b. yang, j. betteridge, a. carlson, b. dalvi, m. gardner, b. kisiel, j. krishnamurthy, n. lao, k. mazaitis, t. mohamed, n. nakashole, e. platanios, a. ritter, m. samadi, b. settles, r. wang, d. wijaya, a. gupta, x. chen, a. saparov, m. greaves, j. welling communications of the acm, 61(5), pp. 103-115, may 2018.. efficient and distributed generalized canonical correlations an*lysis for big multiview data. x. fu , k. huang, e.e. papalexakis, h. song, p. talukdar, n. d. sidiropoulos, c. faloutsos, and t. mitchell