Bay Area Vision Meeting: Unsupervised Feature Learning and Deep Learning - YouTube
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Lessons from neuroscience: one algorithm for all kinds of learning
Looking for better representations of the input (features)
Feature learning via sparse coding (sparse linear combinations. Edge detection, quantitatively similar to primary visual cortex)
Then learning features hierarchies (several layers. "sparse DBN" "deep belief nets")
Scaling see 25'07 (algos) ; using GPUs
Learning recursive representations. "Generic" hierarchies on text doesn't make sense; learn feature vector that represent sentences
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