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NIPS
2004
15 years 6 months ago
Maximising Sensitivity in a Spiking Network
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
Anthony J. Bell, Lucas C. Parra
JMLR
2008
100views more  JMLR 2008»
15 years 4 months ago
Hit Miss Networks with Applications to Instance Selection
In supervised learning, a training set consisting of labeled instances is used by a learning algorithm for generating a model (classifier) that is subsequently employed for decidi...
Elena Marchiori
EMNLP
2009
15 years 2 months ago
Parser Adaptation and Projection with Quasi-Synchronous Grammar Features
We connect two scenarios in structured learning: adapting a parser trained on one corpus to another annotation style, and projecting syntactic annotations from one language to ano...
David A. Smith, Jason Eisner
AIEDU
2010
14 years 11 months ago
Scaffolding Meta-Cognitive Skills for Effective Analogical Problem Solving via Tailored Example Selection
Although worked-out examples play a key role in cognitive skill acquisition, research demonstrates that students have various levels of meta-cognitive abilities for using examples ...
Kasia Muldner, Cristina Conati
CORR
2010
Springer
210views Education» more  CORR 2010»
15 years 4 months ago
Exploiting Statistical Dependencies in Sparse Representations for Signal Recovery
Signal modeling lies at the core of numerous signal and image processing applications. A recent approach that has drawn considerable attention is sparse representation modeling, in...
Tomer Faktor, Yonina C. Eldar, Michael Elad
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