Timeline for How's string theory phenomenology doing these days (2021)?
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| Jan 8, 2021 at 20:18 | vote | accept | Retracted | ||
| Jan 3, 2021 at 16:44 | history | edited | Ramiro Hum-Sah | CC BY-SA 4.0 |
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| Jan 1, 2021 at 9:03 | comment | added | Ramiro Hum-Sah | @Retracted Consider the problem of finding hamiltonian paths on a graph (an NP-hard problem). Suppose that we have discovered that a deep learning scheme (plus some assumptions and biases over the inputs) is good enough to construct hamiltonian paths in graphs of size 17x17. Does that imply that you have reduced an NP-Problem to a problem in P? No, and that's because that procedure is not general, and does not provide answers with arbitrary high precision. Something similar happens here. The procedure is not as effective as we would want and is not general; it simply gives interesting results. | |
| Jan 1, 2021 at 8:41 | comment | added | Ramiro Hum-Sah | @Retracted I'm definitely not claiming at all that deep learning can solve arbitrary NP -problems in reasonable (polynomial) time. What I'm showing is a concrete example in which particular deep learning architectures, equipped with a set of reasonable assumptions, are shown to be efficient in predicting the answers for a very specific NP-hard problem. Does that imply that we can use those schemes to predict geometric properties of a given CY with arbitrary precision? No. The observation is just that Deep learning does his work, well enough, to deserve the attention of the experts. | |
| Jan 1, 2021 at 8:33 | history | edited | Ramiro Hum-Sah | CC BY-SA 4.0 |
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| Jan 1, 2021 at 4:13 | comment | added | Retracted | Deep learning can't get around NP-completeness, it's not magic. If deep learning works, then the problem must have some "not really NP complete" character, for example it may be possible to have arbitrarily good approximations in polynomial time. | |
| Dec 31, 2020 at 19:21 | history | edited | Ramiro Hum-Sah | CC BY-SA 4.0 |
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| Dec 31, 2020 at 17:16 | history | answered | Ramiro Hum-Sah | CC BY-SA 4.0 |