siamese

I mean I'm sure they had to implement new code to make this silly two-button Siamese construct exist.

Or doing ergonomic design based on the average number of heads being well above one (think of Siamese twins).

Also the strings in Spaceboy from Siamese Dream, for all you other 35 year olds out there...

Those that do vary (e.g, Siamese cats) differ by small amounts in comparison to dog breeds.

The US and China are about as far from war as Siamese twins are from schizophrenia!

Our senior Siamese tips over anything full of water, and that zapped the wifey's Mac.

I remember feeling the same way about Siamese Dream when all the jocks suddenly got into the Smashing Pumpkins.

He was the terror of the local cats and dogs, but left our two siamese alone because he knew they were part of the family.

Billy Corgan of the Smashing Pumpkins switched from EL-34's to Soviet KT-88 tubes in his Marshall giving him that super creamy distortion used in Siamese Dream.

Look down at the picture of the Siamese cat.

Maybe in the future the AI in charge of the in vitro fertilized human factory will uncover the genes to induce siamese attached at the brain twins.

I've run my share of siamese coax for low-voltage-fed analog cameras, and also separate power for low-voltage Ethernet-connected cameras, and I'm completely over those concepts.

The features are either passed through a softmax layer or used as a "siamese" network.

"Not straying away" could be addressed by a Siamese network classifier that that a pair of documents and tests "Is B relevant to A?" you could synthesize training data for that by pairing real replies and also pairing random documents.

"It's bad to breathe molecule #346739572384143 of oxygen, because if everyone would breathe molecule #346739572384143 of oxygen, we'd all share the same lungs and be some kind of Siamese octbillionuplets" The categorical imperative says: decide rules that would be good if everybody followed them, and then follow those rules.

(Siamese networks provide a similarity metric as opposed to a probability distribution over labels.[3]) As far as recognition goes, most modern algorithms don't use fiducials (what you call 'nodal points') to do recognition.