This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
structured_factorization [2018/04/23 18:51]
structured_factorization [2018/10/27 12:07] (current)
Line 337: Line 337:
 https://​papers.nips.cc/​paper/​3904-guaranteed-rank-minimization-via-singular-value-projection.pdf ​ https://​papers.nips.cc/​paper/​3904-guaranteed-rank-minimization-via-singular-value-projection.pdf ​
 +https://​arxiv.org/​abs/​1805.04582v1 TensOrMachine:​ Probabilistic Boolean Tensor Decomposition
 +https://​arxiv.org/​abs/​0909.4061 Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
 +https://​arxiv.org/​abs/​1802.05983v2 Disentangling by Factorising
 +https://​arxiv.org/​pdf/​1810.10531.pdf A mathematical theory of semantic development
 +in deep neural networks
 +The synaptic weights of the neural
 +network extract from the statistical structure of the environment
 +a set of paired object analyzers and feature synthesizers associated
 +with every categorical distinction. The bootstrapped,​ simultaneous
 +learning of each pair solves the apparent Gordian knot of knowing
 +both which items belong to a category, and which features are important
 +for that category: the object analyzers determine category
 +membership, while the feature synthesizers determine feature importance,
 +and the set of extracted categories are uniquely determined
 +by the statistics of the environment.