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NIPS
2003
15 years 6 months ago
Minimax Embeddings
Spectral methods for nonlinear dimensionality reduction (NLDR) impose a neighborhood graph on point data and compute eigenfunctions of a quadratic form generated from the graph. W...
Matthew Brand
NN
1998
Springer
108views Neural Networks» more  NN 1998»
15 years 4 months ago
How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies
Learning long-term temporal dependencies with recurrent neural networks can be a difficult problem. It has recently been shown that a class of recurrent neural networks called NA...
Tsungnan Lin, Bill G. Horne, C. Lee Giles
CODES
2008
IEEE
15 years 7 months ago
Application specific non-volatile primary memory for embedded systems
Memory subsystems have been considered as one of the most critical components in embedded systems and furthermore, displaying increasing complexity as application requirements div...
Kwangyoon Lee, Alex Orailoglu
ML
2008
ACM
15 years 5 months ago
Incremental exemplar learning schemes for classification on embedded devices
Although memory-based classifiers offer robust classification performance, their widespread usage on embedded devices is hindered due to the device's limited memory resources...
Ankur Jain, Daniel Nikovski
ICIP
2004
IEEE
16 years 6 months ago
The seamlessly multiplexed embedded codec (SMEC) and its application in image coding
The landmark JPEG 2000 image compression standard offers not only superior compression performance, but also incredible flexibility. The compressed bitstream of JPEG 2000 can be f...
Jin Li