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» How Well Can Primal-Dual and Local-Ratio Algorithms Perform
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ESANN
2000
15 years 2 months ago
SpikeProp: backpropagation for networks of spiking neurons
Abstract. For a network of spiking neurons with reasonable postsynaptic potentials, we derive a supervised learning rule akin to traditional error-back-propagation, SpikeProp and s...
Sander M. Bohte, Joost N. Kok, Johannes A. La Pout...
KDD
2006
ACM
164views Data Mining» more  KDD 2006»
16 years 1 months ago
Sampling from large graphs
Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.)...
Jure Leskovec, Christos Faloutsos
JMLR
2010
148views more  JMLR 2010»
14 years 8 months ago
A Generalized Path Integral Control Approach to Reinforcement Learning
With the goal to generate more scalable algorithms with higher efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towards combining classical tec...
Evangelos Theodorou, Jonas Buchli, Stefan Schaal
ICML
2007
IEEE
16 years 2 months ago
Online discovery of similarity mappings
We consider the problem of choosing, sequentially, a map which assigns elements of a set A to a few elements of a set B. On each round, the algorithm suffers some cost associated ...
Alexander Rakhlin, Jacob Abernethy, Peter L. Bartl...
CDC
2008
IEEE
15 years 8 months ago
Simultaneous placement and assignment for exploration in mobile backbone networks
Abstract— This paper presents new algorithms for conducting cooperative sensing using a mobile backbone network. This hierarchical sensing approach combines backbone nodes, which...
Emily M. Craparo, Jonathan P. How, Eytan Modiano