Topic modeling with LDA: MLlib meets GraphXUnified governance for all data, analytics and AI assets
Scalable Distributed Inference of Dynamic User Interests for Behavioral Targeting
Amr Ahmed is a Senior Staff Research Scientist at Google. He received his M.Sc and PhD degrees from the School of Computer Science, Carnegie Mellon University in 2009 and 2011, respectively. He received the best paper award at KDD 2014 , the best Paper Award at WSDM 2014, the 2012 ACM SIGKDD Doctoral Dissertation Award, and a best paper award (runner-up) at WSDM 2012. He co-chaired the WWW'18 trac
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PLDA+: Parallel latent dirichlet allocation with data placement and pipeline processing
PLDA+: Parallel Latent Dirichlet Allocation with Data Placement and Pipeline Processing ZHIYUAN LIU, YUZHOU ZHANG, and EDWARD Y. CHANG, Google Inc. MAOSONG SUN, Tsinghua University Previous methods of distributed Gibbs sampling for LDA run into either memory or communication bottlenecks. To improve scalability, we propose four strategies: data placement, pipeline processing, word bundling, and p
Latent Dirichlet Allocation (LDA) and Google's Rankings are Remarkably Well Correlated
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自然言語処ç†ç ”究会 - tsubosakaã®æ—¥è¨˜
id:nokunoã•ã‚“ãŒä¸»å®°ã™ã‚‹ç¬¬2回自然言語処ç†å‹‰å¼·ä¼šï¼ æ±äº¬ã§"Latent Dirichlet Allocation入門"ã¨ã„ã†ã‚¿ã‚¤ãƒˆãƒ«ã§ç™ºè¡¨ã—ã¦ãã¾ã—ãŸã€‚ 内容ã¨ã—ã¦ã¯æ©Ÿæ¢°å¦ç¿’ライブラリMalletã«å®Ÿè£…ã•ã‚Œã¦ã„ã‚‹LDAã®ãƒžãƒ«ãƒã‚¹ãƒ¬ãƒƒãƒ‰å®Ÿè£…クラスã®ParallelTopicModelã§ä½¿ã‚ã‚Œã¦ã„るトピックモデルã®æŠ€è¡“を紹介ã™ã‚‹ã¨ã„ã†è©±ã§ã—ãŸã€‚ Latent Dirichlet Allocation入門View more presentations from tsubosaka. 本当ã¯æ–‡ç« 検索ã¸ã®å¿œç”¨ã¨ã‹ã®è©±ã‚‚ã—ãŸã‹ã£ãŸã®ã§ã™ãŒæº–å‚™ã«æ™‚é–“ãŒè¶³ã‚Šãšæ–念
[機械å¦ç¿’] CVB0を実装ã—ã¦ã¿ãŸ - tsubosakaã®æ—¥è¨˜
On Smoothing and Inference for Topic Models (UAI 2009) pdfã§è¿°ã¹ã‚‰ã‚Œã¦ã„ã‚‹LDAã®æŽ¨è«–方法ã§ã‚ã‚‹CVB0を実装ã—ã¦ã¿ãŸã€‚ ã“ã‚Œã¯Tehらã®CVBã§è¿°ã¹ã‚‰ã‚Œã¦ã„る期待値ã®2次ã®é …ã¾ã§ã®è¿‘ä¼¼ã®éƒ¨åˆ†ã‚’ã•ã‚‰ã«è¿‘ä¼¼ã—ã¦0次ã®é …ã ã‘ã§è¿‘ä¼¼ã—ãŸã‚‚ã®ã¨ãªã£ã¦ã„る。二次近似ã«æ¯”ã¹expã®è¨ˆç®—ãŒã„らãªã„分ã€é«˜é€Ÿã«å®Ÿè¡Œã™ã‚‹ã“ã¨ãŒã§ãる。 手元ã§å®Ÿé¨“ã—ãŸã¨ã“ã‚å‰å›žã®è¨˜äº‹ã§å®Ÿè£…ã—ãŸCGS(Collapsed Gibbs samplerより2å€ç¨‹åº¦é«˜é€Ÿã§ã‚ã£ãŸã€‚ Token.java å‰å›žã®ã‚‚ã®ã«æ–‡ç« é‡ã¿ã®é …ãŒä»˜ã‘åŠ ã‚ã£ã¦ã„ã‚‹ public class Token { public int docId; public int wordId; public double weight; public Token(int d , int w ){ docId =
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[機械å¦ç¿’] トピックモデル関係ã®è«–文メモ - tsubosakaã®æ—¥è¨˜
最近èªã‚“ã トピックモデル関係ã®è«–æ–‡ã®ã–ã£ã¨ã—ãŸãƒ¡ãƒ¢ã€‚内容ã«ã¤ã„ã¦ã¯é–“é•ã£ã¦ç†è§£ã—ã¦ã„ã‚‹ã¨ã“ã‚も多々ã‚ã‚‹ã¨æ€ã„ã¾ã™ã€‚ (追記 12/24) 最後ã®ã»ã†ã«è«–文をèªã‚€åŸºç¤Žã¨ãªã‚‹æ–‡çŒ®ã‚’è¿½åŠ ã—ã¾ã—ãŸã€‚ Efficient Methods for Topic Model Inference on Streaming Document Collections (KDD 2009) è«–æ–‡ã®è©±ã¯2ã¤ã‚ã£ã¦ä¸€ã¤ç›®ãŒSparseLDAã¨ã„ã†Collapsed Gibbs samplerã®çœãƒ¡ãƒ¢ãƒªã‹ã¤é«˜é€Ÿãªæ–¹æ³•ã®æ案ã¨2ã¤ç›®ã¯ã‚ªãƒ³ãƒ©ã‚¤ãƒ³ã§æ–‡ç« ãŒå…¥åŠ›ã•ã‚Œã‚‹ã‚ˆã†ãªå ´åˆã«ãŠã„ã¦è¨“練データã¨æ–°è¦ãƒ‡ãƒ¼ã‚¿ã‚’ã©ã†ä½¿ã†ã‹ã¨ã„ã†æˆ¦ç•¥ã«ã¤ã„ã¦è¿°ã¹ã¦å®Ÿé¨“ã—ã¦ã„る。 Collapsed Gibbs samplerを高速化ã—よã†ã¨ã„ã†è«–æ–‡ã¯Porteous et al.(KDD 2008)ã§ã‚‚è¿°ã¹ã‚‰ã‚Œã¦ã„ã‚‹ã‘ã©ãれよりも2å€ãらã„高速(通
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Collaborative Filtering for Orkut Communities: Discovery of User Latent Behavior - WWW2009 EPrints
AbstractUsers of social networking services can connect with each other by forming communities for online interaction. Yet as the number of communities hosted by such websites grows over time, users have even greater need for effective commu- nity recommendations in order to meet more users. In this paper, we investigate two algorithms from very different do- mains and evaluate their effectiveness
NIPS 2008 Proceedings
Advances in Neural Information Processing Systems 21 (NIPS 2008) The papers below appear in Advances in Neural Information Processing Systems 21 edited by D. Koller and D. Schuurmans and Y. Bengio and L. Bottou. They are proceedings from the conference, "Neural Information Processing Systems 2008." Structure Learning in Human Sequential Decision-Making Daniel Acuna, Paul R. Schrater The Gaussian P
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2024.09.02： ã€è¨˜äº‹æ›´æ–°ã€‘ç§ã®ãƒ–ックマーク「AIアライメント〠  →詳細 2024.09.02： ã€ä¼šèªŒç™ºè¡Œã€‘人工知能å¦ä¼šèªŒ Vol.39 No.5 (2024/9)   →詳細 2024.08.29： ã€å‹Ÿé›†æ¡ˆå†…】2024年度人工知能å¦ä¼šã‚³ãƒ³ãƒšãƒ†ã‚£ã‚·ãƒ§ãƒ³é–‹å‚¬æ”¯æ´åˆ¶åº¦å‹Ÿé›†è¦é ˜ï¼ˆç§‹å‹Ÿé›†ï¼‰   →詳細 2024.08.09： ã€å‚åŠ å‹Ÿé›†ã€‘ç¬¬129回人工知能基本å•é¡Œç ”究会(SIG-FPAI)，2024/9/5-6 ãƒã‚¤ãƒ–リッド   →詳細 2024.08.06： ã€ãŠçŸ¥ã‚‰ã›ã€‘次世代ã®ãŸã‚ã®2024å¹´å¤AIセミナー å‚åŠ ç”³è¾¼8月15æ—¥ã¾ã§ï¼ˆåå¤å±‹ã€æ±äº¬ã€äº¬éƒ½ï¼‰   →詳細
GibbsLDA++: A C/C++ Implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling for Parameter Estimation and Inference
GibbsLDA++: A C/C++ Implementation of Latent Dirichlet Allocation GibbsLDA++ is a C/C++ implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling technique for parameter estimation and inference. It is very fast and is designed to analyze hidden/latent topic structures of large-scale datasets including large collections of text/Web documents. LDA was first introduced by David Blei e
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Google Code Archive - Long-term storage for Google Code Project Hosting.
https://mail-archives.apache.org/mod_mbox/lucene-mahout-dev/200905.mbox/%3C1234137613.1243105785963.JavaMail.jira@brutus%3E
plda - Google Code
fp224-phan.dvi
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