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ICML
2010
IEEE
15 years 2 months ago
Learning Markov Logic Networks Using Structural Motifs
Markov logic networks (MLNs) use firstorder formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (4-5 literals) due to extre...
Stanley Kok, Pedro Domingos
ICDE
2006
IEEE
163views Database» more  ICDE 2006»
16 years 3 months ago
A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks
Wireless sensor networks generate a vast amount of data. This data, however, must be sparingly extracted to conserve energy, usually the most precious resource in battery-powered ...
Adam Silberstein, Carla Schlatter Ellis, Jun Yang ...
92
Voted
ICDM
2009
IEEE
152views Data Mining» more  ICDM 2009»
15 years 8 months ago
SLIDER: Mining Correlated Motifs in Protein-Protein Interaction Networks
Correlated motif mining (CMM) is the problem to find overrepresented pairs of patterns, called motif pairs, in interacting protein sequences. Algorithmic solutions for CMM thereb...
Peter Boyen, Frank Neven, Dries Van Dyck, Aalt-Jan...
IJCNN
2007
IEEE
15 years 8 months ago
System Identification for the Hodgkin-Huxley Model using Artificial Neural Networks
— A single biological neuron is able to perform complex computations that are highly nonlinear in nature, adaptive, and superior to the perceptron model. A neuron is essentially ...
Manish Saggar, Tekin Meriçli, Sari Andoni, ...
TON
2002
144views more  TON 2002»
15 years 1 months ago
Algorithms for provisioning virtual private networks in the hose model
Virtual Private Networks (VPNs) provide customers with predictable and secure network connections over a shared network. The recently proposed hose model for VPNs allows for great...
Amit Kumar, Rajeev Rastogi, Abraham Silberschatz, ...