LSTM iliyo na Usimamizi dhaifu
LSTM iliyo na usimamizi dhaifu hufunza mtandao wa Long Short-Term Memory (LSTM) kwenye data za mlolongo ambapo lebo safi, zilizowekwa alama kwa mikono ni chache au hazipo. Badala yake, vyanzo vingi vya lebo visivyo kamili — sheria za heuristiki, usimamizi wa mbali, crowdsourcing, au utendaji wa kuweka lebo kwa programu — huunganishwa ili kutoa lebo za mafunzo za uwezekano, ambazo kisha hutumiwa kusimamia LSTM. Hii inaruhusu mafunzo yanayoweza kuongezwa kwa wingi kwenye makusanyo makubwa yasiyo na lebo bila kuweka alama kwa mikono kwa kina.
Soma mbinu kamili
Ingia kwa akaunti ya bure ili kusoma sehemu hii.
Method map
The neighbourhood of related methods — select a node to explore.
Vyanzo
- Ratner, A., De Sa, C., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗
- Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53. DOI: 10.1093/nsr/nwx106 ↗
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 3). Weakly Supervised Long Short-Term Memory Network. ScholarGate. https://scholargate.app/sw/deep-learning/weakly-supervised-lstm
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- LSTM IliyorekebishwaUjifunzaji wa Kina↔ compare
- Long Short-Term Memory (LSTM)Ujifunzaji wa Kina↔ compare
- Mtandao wa Nyuro UnaojirudiaUjifunzaji wa Kina↔ compare
- LSTM nusu-simamiziUjifunzaji wa Kina↔ compare
- RNNi iliyofunzwa kwa udhaifuUjifunzaji wa Kina↔ compare
- Transformer ya Usimamizi dhaifuUjifunzaji wa Kina↔ compare
Imerejelewa na
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