Arxiv InsightsMy name is Xander Steenbrugge, and I read a ton of papers on Machine Learning and AI. But papers can be a bit dry & take a while to read. And we are lazy rig...
Recent Trends in Deep Learning Based Natural Language Processing
Deep learning methods employ multiple processing layers to learn hierarchical representations of data and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP ta
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Spectral Learning for Supervised Topic Models
Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document. Existing inference methods are based on variational approximation or Monte Carlo sampling, which often suffers from the local minimum defect. Spectral methods have been applied to learn unsupervised topic models, such as latent Dirichlet a
Automatic Variational ABC
Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models. Stochastic Variational inference (SVI) is an appealing alternative to the inefficient sampling approaches commonly used in ABC. However, SVI is highly sensitive to the variance of the gradient estimators, and this problem is exacerbated by approximating the likelihood. We
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科å¦èªŒã«æŠ•ç¨¿ã•ã‚ŒãŸå¦è¡“論文をèªã‚€ã«ã¯è³¼èªæ–™ã‚’支払ã†å¿…è¦ãŒã‚ã‚Šã€ã“ã‚ŒãŒèª°ã‚‚ãŒç§‘å¦ã«è§¦ã‚Œã‚‰ã‚Œã‚‹æ©Ÿä¼šã‚’制é™ã—ã¦ã„ã‚‹ã¨ã„ã†æ‰¹åˆ¤çš„ãªç²¾ç¥žã‹ã‚‰ã€ä¸–ç•Œä¸ã®å¦è¡“論文を公開ã—ã¦ã„る海賊版サイトã¨ã—ã¦ã€ŒSci-Hubã€ãŒçŸ¥ã‚‰ã‚Œã¦ã„ã¾ã™ã€‚Sci-Hubã¯è«–æ–‡ãŒãƒ€ã‚¦ãƒ³ãƒãƒ¼ãƒ‰ã•ã‚ŒãŸçŠ¶æ³ã«ã¤ã„ã¦ã®ãƒ‡ãƒ¼ã‚¿ã‚’公開ã—ã¦ãŠã‚Šã€ã“ã®ãƒ‡ãƒ¼ã‚¿ã‚’詳ã—ã調ã¹ã‚‹ã¨ã€ã©ã®ã‚ˆã†ãªå›½ã§ãƒ€ã‚¦ãƒ³ãƒãƒ¼ãƒ‰ãŒå¤šã„ã®ã‹ã€è«–æ–‡ãŒé–²è¦§ã•ã‚Œã‚„ã™ã„時間帯ã¯ã„ã¤ãªã®ã‹ãªã©ã‚’知るã“ã¨ãŒã§ãã¾ã™ã€‚ The Winnower | Correlating the Sci-Hub data with World Bank Indicators and Identifying Academic Use https://thewinnower.com/papers/4715-correlating-the-sci-hub-data-with-world-bank-indicat
CMU教授直ä¼ã®è«–æ–‡ã®æ›¸ãæ–¹ - ã§ã‹ã„ãƒãƒ¼ã‚ºã‚’ベーグルã™ã‚‹
CMUã«ç•™å¦ã—ã¦ã„る時ã«Faloutsos教授ã«æ•™ã‚ã£ãŸè«–æ–‡ã®æ›¸ã方をã¾ã¨ã‚る。ã“ã®æ›¸ãæ–¹ã«å¾“ã†ã“ã¨ã§è«–æ–‡ã®æŽ¡æŠžçŽ‡ãŒã‹ãªã‚Šä¸ŠãŒã£ãŸã€‚今ã¨ãªã£ã¦ã¯è‡ªåˆ†çš„ã«å½“ãŸã‚Šå‰ã®ã“ã¨ã ã—ã€ã§ãã‚‹ç ”ç©¶è€…ã®çš†æ§˜ã¯è‡ªç„¶ã¨å®ˆã£ã¦ã„ã‚‹ã“ã¨ã‚‚多ã„ã¨æ€ã†ã‘ã©è‰¯ã„論文を書ããŸã„ã¨æ€ã£ã¦ã„ã‚‹å¦ç”Ÿã¨ã‹ã«å‚考ã«ã—ã¦ã‚‚らãˆãŸã‚‰ã¨æ€ã†ã€‚ãŸã ã—ã€Faloutsos教授ã«æ•™ãˆã¦ã‚‚らã£ãŸã“ã¨ã‚’一旦自分ã§å™›ã¿ç •ã„ã¦ã‹ã‚‰ã¾ã¨ã‚ãŸã‚‚ã®ãªã®ã§è‡ªåˆ†ã®ä¸»è¦³ã¨ã‹ãŒæ··ã˜ã£ã¦ã—ã¾ã£ã¦ã„ã‚‹ã‹ã‚‚ã—ã‚Œãªã„。 主語ãŒå¤§ãããªã‚‰ãªã„よã†ã«äºˆã‚æ–ã£ã¦ãŠãã‘ã©ã€ã“ã®æ›¸ãæ–¹ã¯ã‚‚ã¡ã‚ã‚“ã™ã¹ã¦ã®è«–æ–‡ã«å¯¾ã—ã¦å½“ã¦ã¯ã¾ã‚‹ã‚ã‘ã˜ã‚ƒãªãã¦ä»¥ä¸‹ã®å‰ææ¡ä»¶ãŒã‚る。 国際会è°è«–æ–‡ã§ã‚ã‚‹ データマイニング関連分野ã®è«–æ–‡ã§ã‚ã‚‹ 論文誌ã¨ã‹å’è«–ã¨ã‹ã‚‚ã£ã¨é•·ã‚ã®è«–文を書ãã¨ãã¯å½“ã¦ã¯ã¾ã‚‰ãªã„é …ç›®ã‚‚ã‚ã‚‹ã—ã€ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°é–¢é€£åˆ†é‡Žä»¥å¤–ã®è«–文を書ã„ãŸã“ã¨ãŒç„¡ã„ã®ã§ãれ以外ã®åˆ†é‡Žã®è«–æ–‡ã«å½“ã¦ã¯ã¾ã‚‹ã‹ã‚‚
A Year in Papers | the morning paper
the morning paper a random walk through Computer Science research, by Adrian Colyer Made delightfully fast by strattic We’ve reached the end of term again, and I’m taking a break from writing up papers over the holidays – a chance to replenish my backlog and start planning for 2016 too! I want to see what I can do to improve the readability of the site as well. The Morning Paper will resume on the
CiteSpace: visualizing patterns and trends in scientific literature
Chen, C. et al. (2010) The structure and dynamics of co-citation clusters: A multiple-perspective co-citation analysis. Journal of the American Society for Information Science and Technology. (10.1002/asi.21309) Chen, C. (2006) CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technol
Scalability! But at what COST? | USENIX
We offer a new metric for big data platforms, COST, or the Configuration that Outperforms a Single Thread. The COST of a given platform for a given problem is the hardware configuration required before the platform outperforms a competent single-threaded implementation. COST weighs a system’s scalability against the overheads introduced by the system, and indicates the actual performance gains of
英語論文ã®ãŸã‚ã®snack writing - ãŸã‘ã¡ã‚…ã•ã‚“å®…
「アメリカãƒã‚¹ãƒ‰ã‚¯ã®æ©ãæ–¹ã€ã•ã‚“より 英語論文を書ã続ã‘ã‚‹ã«ã¯ï¼šSnack writingã®ã™ã™ã‚ http://kengg.blog75.fc2.com/blog-entry-244.html ã“ã®ãƒ–ãƒã‚°ã‚’見ã¤ã‘ãŸãã£ã‹ã‘ãŒã€ã“ã®è¨˜äº‹ã€‚ 「英語論文ã®ã‚³ãƒ„ã©ã“ã‹ã«è½ã¡ã¦ãーã‹ãªã€ã¨ã„ã†æ°—æŒã¡ã§ãƒ—ラプラã€ãƒãƒƒãƒˆã‚µãƒ¼ãƒ•ã‚£ãƒ³ã‚’ã—ã¦ã„ã¦è¦‹ã¤ã‘ãŸã®ãŒã“ã®ãƒ–ãƒã‚°ã€‚ãã®ã¨ãã«ç™ºè¦‹ã—ãŸè¨˜äº‹ãŒã“れ。 Snack writing ã¨ã«ã‹ã何ã§ã‚‚ã„ã„ã‹ã‚‰ä¸€æ—¥ã«æ±ºã¾ã£ãŸæ™‚é–“ã«è‹±èªžè«–文を書ãç™–ã‚’ã¤ã‘ã‚‹ ãれを実践ã—ã¦ã¯ã‚„åŠå¹´ã€‚ 個人的ã«ã¯ã€è‹±èªžã‚’書ã力ãŒå°‘ã—上昇ã—ãŸã‚ˆã†ã«æ€ãˆã¾ã™ã€‚ ã‚„ã‚Šæ–¹ã¯ç°¡å˜ã§ã€‚ １．決ã¾ã£ãŸæ™‚é–“ã«è‹±èªžã‚’書ã 自分ã§ã‚ã‚Œã°åˆå¾Œï¼‘６時。一番é ãŒå†´ãˆã¦ã„ã‚‹ã‹ã‚‰ã€‚ãã†æ€ã£ã¦ã„ãŸã‚‰ã€å…ˆæ—¥ã€åˆå¾Œã®æ–¹ãŒé ãŒåƒãã¨ã„ã†è«–文をèªã‚“ã 。 ２．推敲ã¯ã—ãªã„ ã“れ絶対。推敲をã¯ã˜ã‚ã‚‹ã¨æ¢ã¾ã‚‰ãªããªã£ã¦ã—ã¾ã†ã€‚ãã—ã¦ã€
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https://dl.acm.org/ccs
Time-series-database-world
Non-Invarsive Progressive Optimization for In-Memory Databases
https://dawenl.github.io/publications/LiangACB16-cofactor.pdf
An insider’s guide to author responses
https://www.logos.t.u-tokyo.ac.jp/~hassy/deep_learning/arxiv/
Constrained Bayesian Inference for Low Rank Multitask Learning
Asynchronous Complex Analytics in a Distributed Dataflow Architecture
Scholarly Information Extraction | CiteSeerX
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