On the Dynamics of Machine Learning Algorithms and Behavioral Game TheoryRikiya Takahashi
Presentation Material used in guest lecturing at University of Tsukuba on September 17, 2016.
Target audience is part-time PhD student working at a machine learning, data mining, or agent-based simulation project.
On the Dynamics of Machine Learning Algorithms and Behavioral Game TheoryRikiya Takahashi
Presentation Material used in guest lecturing at University of Tsukuba on September 17, 2016.
Target audience is part-time PhD student working at a machine learning, data mining, or agent-based simulation project.
大規模データセットでの推論に便利なSVIの概要をまとめました.
SVIは確率的最適化の枠組みで行う変分ベイズ法です.
随時更新してます.
参考文献
[1]Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley. Stochastic variational inference. The Journal of Machine Learning Research, Vol. 14, No. 1, pp. 1303–1347, 2013.
[2] 佐藤一誠. トピックモデルによる統計的意味解析. コロナ社, 2015.
sublabel accurate convex relaxation of vectorial multilabel energiesFujimoto Keisuke
This document summarizes a presentation on the paper "Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies". It discusses how the paper proposes a method to efficiently solve high-dimensional, nonlinear vectorial labeling problems by approximating them as convex problems. Specifically, it divides the problem domain into subregions and approximates each subregion with a convex function, yielding an overall approximation that is still non-convex but with higher accuracy. This lifting technique transforms the variables into a higher-dimensional space to formulate the data and regularization terms in a way that allows solving the problem as a convex optimization.
Fractality of Massive Graphs: Scalable Analysis with Sketch-Based Box-Coverin...Kenko Nakamura
This document proposes a sketch-based box-covering algorithm to efficiently analyze the fractality of massive graphs. It summarizes that some real-world networks have been found to be fractal in nature, but existing algorithms for determining fractality are too slow for large networks. The proposed method uses min-hash to represent boxes implicitly and solves the box-covering problem efficiently in the sketch space using a binary search tree and heap, allowing fractality analysis of networks with millions of edges for the first time.
PFN福田圭祐による東大大学院「融合情報学特別講義Ⅲ」(2022年10月19日)の講義資料です。
・Introduction to Preferred Networks
・Our developments to date
・Our research & platform
・Simulation ✕ AI
60. rank2(T, 12)
0123456789012345
T = 0721436725047263
s (節点の開始位置) e (節点の開始位置)
Bd : d番⽬目のbit列列
0100101101011010 nzd : Bd-1の0の数
s = 0, e = pos
0101101110110011 for d = 0 to log2s
b = cのd番⽬目の上位bit
s = rankb(Bd, s)
0100100100110110 e = rankb(Bd, e)
if (b == 1)
0044222661533777 s += nzd, e += nzd
end if
end for
最後の数は説明のために⽤用意 60
return e - s
67. 参考⽂文献
l “⾼高速⽂文字列列解析の世界”, 岡野原 ⼤大輔, 岩波書店
l “Wavele Trees for All”, G. Navarro, CPM2012
l “The Wavelet Matrix”, F Claude, G. Navarro, SPIRE 2012
.
67