{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:31:57Z","timestamp":1730266317918,"version":"3.28.0"},"reference-count":22,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"DOI":"10.1109\/ijcnn.2018.8489778","type":"proceedings-article","created":{"date-parts":[[2018,10,19]],"date-time":"2018-10-19T22:25:09Z","timestamp":1539987909000},"page":"1-8","source":"Crossref","is-referenced-by-count":4,"title":["Topic Modeling using Variational Auto-Encoders with Gumbel-Softmax and Logistic-Normal Mixture Distributions"],"prefix":"10.1109","author":[{"given":"Denys","family":"Silveira","sequence":"first","affiliation":[]},{"given":"Andr'e","family":"Carvalho","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Cristo","sequence":"additional","affiliation":[]},{"given":"Marie-Francine","family":"Moens","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"Sentence level recurrent topic model: Letting topics speak for themselves","volume":"abs 1604 2038","author":"tian","year":"2016","journal-title":"CoRR"},{"key":"ref11","first-page":"1727","article-title":"Neural variational inference for text processing","author":"miao","year":"2016","journal-title":"ICML’16"},{"key":"ref12","article-title":"Autoencoding variational inference for topic models","author":"srivastava","year":"2017","journal-title":"arXiv preprint arXiv 1703 01488"},{"key":"ref13","article-title":"Deep unsupervised clustering with gaussian mixture variational autoencoders","volume":"abs 1611 2648","author":"dilokthanakul","year":"2016","journal-title":"CoRR"},{"journal-title":"Categorical reparameterization with gumbel-softmax","year":"2016","author":"jang","key":"ref14"},{"journal-title":"Pattern Recognition and Machine Learning","year":"2006","author":"bishop","key":"ref15"},{"key":"ref16","article-title":"Adam: A method for stochastic optimization","volume":"abs 1412 6980","author":"kingma","year":"2014","journal-title":"CoRR"},{"key":"ref17","first-page":"2172","article-title":"InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets","author":"chen","year":"2016","journal-title":"NIPS’16"},{"key":"ref18","first-page":"3621","article-title":"Scaling memory-augmented neural networks with sparse reads and writes","author":"rae","year":"2016","journal-title":"NIPS’16"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177728847"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1162\/COLI_a_00173"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-011-5272-5"},{"key":"ref6","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"blei","year":"2003","journal-title":"J Mach Learn Res"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2011.125"},{"key":"ref8","first-page":"2717","article-title":"A neural autoregressive topic model","author":"larochelle","year":"2012","journal-title":"NIPS’12"},{"key":"ref7","first-page":"1607","article-title":"Replicated softmax: an undirected topic model","volume":"22","author":"salakhutdinov","year":"2009","journal-title":"NIPS’09"},{"key":"ref2","article-title":"Integrating document clustering and topic modeling","volume":"abs 1309 6874","author":"xie","year":"2013","journal-title":"CoRR"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1148170.1148204"},{"key":"ref9","first-page":"2353","article-title":"Ordering-sensitive and semantic-aware topic modeling","author":"yang","year":"2015","journal-title":"AAAI’15"},{"key":"ref20","first-page":"3086","article-title":"A* sampling","author":"maddison","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/E14-1056"},{"key":"ref21","article-title":"The concrete distribution: A continuous relaxation of discrete random variables","volume":"abs 1611 712","author":"maddison","year":"2016","journal-title":"CoRR"}],"event":{"name":"2018 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2018,7,8]]},"location":"Rio de Janeiro","end":{"date-parts":[[2018,7,13]]}},"container-title":["2018 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8465565\/8488986\/08489778.pdf?arnumber=8489778","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T04:58:39Z","timestamp":1598245119000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8489778\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1109\/ijcnn.2018.8489778","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}