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NIPS
1992
15 years 7 months ago
A Note on Learning Vector Quantization
Vector Quantization is useful for data compression. Competitive Learning which minimizes reconstruction error is an appropriate algorithm for vector quantization of unlabelled dat...
Virginia R. de Sa, Dana H. Ballard
163
Voted
CGF
2010
115views more  CGF 2010»
15 years 6 months ago
Closed-form Blending of Local Symmetries
We present a closed-form solution for the symmetrization problem, solving for the optimal deformation that reconciles a set of local bilateral symmetries. Given as input a set of ...
Deboshmita Ghosh, Nina Amenta, Michael M. Kazhdan
CGF
2008
110views more  CGF 2008»
15 years 6 months ago
Motorcycle Graphs: Canonical Quad Mesh Partitioning
We describe algorithms for canonically partitioning semi-regular quadrilateral meshes into structured submeshes, using an adaptation of the geometric motorcycle graph of Eppstein ...
David Eppstein, Michael T. Goodrich, Ethan Kim, Ra...
CORR
2010
Springer
133views Education» more  CORR 2010»
15 years 6 months ago
Nonuniform Sparse Recovery with Gaussian Matrices
Compressive sensing predicts that sufficiently sparse vectors can be recovered from highly incomplete information. Efficient recovery methods such as 1-minimization find the sparse...
Ulas Ayaz, Holger Rauhut
152
Voted
CORR
2007
Springer
73views Education» more  CORR 2007»
15 years 6 months ago
Universal Reinforcement Learning
—We consider an agent interacting with an unmodeled environment. At each time, the agent makes an observation, takes an action, and incurs a cost. Its actions can influence futu...
Vivek F. Farias, Ciamac Cyrus Moallemi, Tsachy Wei...