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JMLR
2010
195views more  JMLR 2010»
15 years 2 months ago
Online Learning for Matrix Factorization and Sparse Coding
Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statisti...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
ICML
2010
IEEE
15 years 5 months ago
Learning Efficiently with Approximate Inference via Dual Losses
Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Glo...
GLOBECOM
2010
IEEE
15 years 2 months ago
Need-Based Communication for Smart Grid: When to Inquire Power Price?
In smart grid, a home appliance can adjust its power consumption level according to the realtime power price obtained from communication channels. Most studies on smart grid do not...
Husheng Li, Robert C. Qiu
GECCO
2009
Springer
15 years 9 months ago
The sensitivity of HyperNEAT to different geometric representations of a problem
HyperNEAT, a generative encoding for evolving artificial neural networks (ANNs), has the unique and powerful ability to exploit the geometry of a problem (e.g., symmetries) by enc...
Jeff Clune, Charles Ofria, Robert T. Pennock
GECCO
2009
Springer
146views Optimization» more  GECCO 2009»
15 years 11 months ago
Analyzing the landscape of a graph based hyper-heuristic for timetabling problems
Hyper-heuristics can be thought of as “heuristics to choose heuristics”. They are concerned with adaptively finding solution methods, rather than directly producing a solutio...
Gabriela Ochoa, Rong Qu, Edmund K. Burke