themann9

4 ms·

1.
by themann9 11y ago
Guy, the only thing abundantly clear is that you are full of dung. Your shameless self-promotion may piss-off the police chief here - murbard2, so be more careful. Ten digits better classified, out of 10000? With a structure more complicat
2.
by themann9 11y ago
Not me dude but I like underdogs and outsiders. What u saying sounds right: symmetries, like the ones computed in section 3.8 are used as constraints a.k.a. "quantum numbers" describing the states in the latent layer. That is vin
3.
by themann9 11y ago
reading is not your thing, ah? It is too condensed and have not finished it but sections 1.5 and 2.3 are very explicit about it...
4.
by themann9 11y ago
Modern stochastic/time series analysis has borrowed most of its formalism from quantum mechanics. It is the ignorance and the bigotry that has held neural nets back 30 years, until GPUs came to the rescue. Having gazillions of paramete
5.
by themann9 11y ago
Shooting first and asking question later ain't gonna make u no friends in Compton, murbard2. Wouldn't hold my breath to hear any answers...
6.
by themann9 11y ago
The benefit seems to be shown on the right chart of Fig 8 (bottom line is best descriptor of probability density). Combining classifiers and generative nets is the natural next step, LeCun is working on in in the context of ConvNets - see t
7.
by themann9 11y ago
2. he is explaining stochastic nets as statistics non-equilibrium systems, in analogy with theory of fluctuations, which Einstein allegedly originated 3. drawing analogies with quantum mechanics (wave function = conditional density) can ope