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» Privacy-preservation for gradient descent methods
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SDM
2008
SIAM
177views Data Mining» more  SDM 2008»
10 years 2 months ago
Practical Private Computation and Zero-Knowledge Tools for Privacy-Preserving Distributed Data Mining
In this paper we explore private computation built on vector addition and its applications in privacypreserving data mining. Vector addition is a surprisingly general tool for imp...
Yitao Duan, John F. Canny
ICANN
2001
Springer
10 years 5 months ago
Fast Curvature Matrix-Vector Products
The method of conjugate gradients provides a very effective way to optimize large, deterministic systems by gradient descent. In its standard form, however, it is not amenable to ...
Nicol N. Schraudolph
SCIA
2009
Springer
161views Image Analysis» more  SCIA 2009»
10 years 8 months ago
A Fast Optimization Method for Level Set Segmentation
Abstract. Level set methods are a popular way to solve the image segmentation problem in computer image analysis. A contour is implicitly represented by the zero level of a signed ...
Thord Andersson, Gunnar Läthén, Reiner...
KDD
2007
ACM
142views Data Mining» more  KDD 2007»
11 years 1 months ago
Privacy-preservation for gradient descent methods
Li Wan, Wee Keong Ng, Shuguo Han, Vincent C. S. Le...
ICDM
2010
IEEE
167views Data Mining» more  ICDM 2010»
9 years 11 months ago
Averaged Stochastic Gradient Descent with Feedback: An Accurate, Robust, and Fast Training Method
On large datasets, the popular training approach has been stochastic gradient descent (SGD). This paper proposes a modification of SGD, called averaged SGD with feedback (ASF), tha...
Xu Sun, Hisashi Kashima, Takuya Matsuzaki, Naonori...
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