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Neural Abstract Machines & Program Induction workshop v2 @ ICML 2018
$ ./nampi_selector.sh > nampi_2018 (v2) < nampi_2016 (v1) $ ./faim_workshop.sh ███╗ ██╗ █████╗ ███╗ ███╗██████╗ ██╗ ████╗ ██║██╔══██╗████╗ ████║██╔══██╗██║ ██╔██╗ ██║███████║██╔████╔██║██████╔╝██║ ██║╚██╗██║██╔══██║██║╚██╔╝██║██╔═══╝ ██║ ██║ ╚████║██║ ██║██║ ╚═╝ ██║██║ ██║ ╚═╝ ╚═══╝╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝ v2.0 Neural Abstract Machines & Program Induction v2 { : A Federated Artif
NIPS 2011 Online Papers
Advances in Neural Information Processing Systems 24 (NIPS 2011) The papers below appear in Advances in Neural Information Processing Systems 24 edited by J. Shawe-Taylor and R.S. Zemel and P.L. Bartlett and F. Pereira and K.Q. Weinberger. They are proceedings from the conference, "Neural Information Processing Systems 2011." Maximum Margin Multi-Instance Learning Hua Wang, Heng Huang, Farhad Kama
Exploiting generative models in discriminative classifiers - がくしゅう日記
文献NIPS1998T. Jaakkola, D.Haussler2011/10/15 再読これは何度も読む価値がある文献.Fisherカーネルを, (1) モデルの判別力を高めるような計量(2) 生成モデルから得られたサンプルの自然な比較指標という２つの観点から導いている. (2)が原稿の本文で議論され, (1)は付録に.(2)は, 対数尤度をモデルパラメタで微分したものが, 特定のサンプルの生成にパラメタがどの程度影響するかを測る尺度であることと,統計多様体における自然な計量がFisher計量であることから導かれる.(1)は, ロジスティック回帰において, ラベルの事後確率を計算するときの入力データの内積をスコアで記述するときに, 計量をFisher情報行列にすると, 背後にある生成モデルの下で最良の判別性を持つという性質から正当性を主張している.
Parallel Stochastic Gradient Discent （NIPS読み会にて発表しました） - nokunoの日記
NIPS読み会にて並列SGDについて発表しました。Parallel Stochastic Gradient Discent #nipsreadingView more presentations from nokuno.他の発表の人の資料は以下。slideshareしかない人のは埋込みにしてあります。まずかったら言ってください。NIPS 2010 論文読み会 / [Ding+] t-Logistic Regression #nipsreading - Mi manca qualche giovedi`?NIPS 2010 読む会View more presentations from tsubosaka.Nips yomikai 1226View more presentations from niammain.nips勉強会_Toward Property-Based Classific
NIPS 2010 – Machine Learning (Theory)
I enjoyed attending NIPS this year, with several things interesting me. For the conference itself: Peter Welinder, Steve Branson, Serge Belongie, and Pietro Perona, The Multidimensional Wisdom of Crowds. This paper is about using mechanical turk to get label information, with results superior to a majority vote approach. David McAllester, Tamir Hazan, and Joseph Keshet Direct Loss Minimization for
Parallelized Stochastic Gradient Descent
Advances in Neural Information Processing Systems 23 (NIPS 2010) The papers below appear in Advances in Neural Information Processing Systems 23 edited by J.D. Lafferty and C.K.I. Williams and J. Shawe-Taylor and R.S. Zemel and A. Culotta. They are proceedings from the conference, "Neural Information Processing Systems 2010." Repeated Games against Budgeted Adversaries Jacob D. Abernethy, Manfred
Learning with Orderings
NIPS 2009 Workshop, Hilton Whistler Resort: Diamond Head, Saturday, December 12, 2009 Call for Abstracts Format Speakers References Motivation and Goals Permutations and partial orders as input and output data are ubiquitous. Examples include: Preference elicitation Social choice and voting theory Ranking and search. Tracking and identity management Structure learning for Bayesian networks Mul
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NIPS2016 - Pages 2-6 (1).pdf
Salon des Refusés
Continuous Time Dynamic Topic Models Chong Wang Computer Science Dept. Princeton University Princeton, NJ 08540 David Blei Computer Science Dept. Princeton University Princeton, NJ 08540 David Heckerman Microsoft Research One Microsoft Way Redmond, WA 98
Multilingual Topic Models for Unaligned Text Jordan Boyd-Graber 35 Olden Street Computer Science Dept. Princeton University Princeton, NJ 08540 David M. Blei 35 Olden Street Computer Science Dept. Princeton University Princeton, NJ 08540 Abstract We deve
nips.dvi
https://people.eecs.berkeley.edu/~jordan/nips-tutorial05.ps
http://www.cs.umass.edu/~mccallum/papers/factorie-nips09.pdf
NIPS*03 One-day Workshop on Nonparametric Bayesian methods and Infinite models
http://www0.cs.ucl.ac.uk/staff/M.Pontil/reading/kernels4MTL.pdf
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data