InoteveraGaidhi rinotevera
Gaussian Musanganiswa Models
Tekinoroji
Nhungamiro yehunyanzvi
Gaussian Maitiro
Gaussian process inzira inoshanduka, isiri parametric yekuenzanisira mabasa anouya neakavakirwa-mukati fungidziro yekusavimbika.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
It is prized when data is scarce and knowing how confident the model is matters as much as the prediction itself.
Kudzika Kwakadzika
A Gaussian Process (GP) inotsanangura mukana wekugovera pamusoro pemafuction pane kuenderana nemaparamita akatarwa. Pakare, chero seti inogumira yemapoinzi akatorwa kubva kuGP anotevera kugovera kwaGaussian (zvakajairika). Iwe unotsanangura zvinoreva basa uye, crucially, covariance kana kernel basa rinokodha kuti zvakafanana zvinobuda zvinofanirwa kunge zvakaita sei kune zviri pedyo. Mushure mekugadzirisa pane zvakacherechedzwa data, iyo GP inodzosa kwete chete kukosha kwakafanotaurwa pane imwe neimwe nyowani asi kuzara kwekufanotaura kugovera, ichipa chirevo uye yakagadziriswa nguva yekuvimba iyo inowedzera kure nedata. Sarudzo yekernel, senge RBF yakatsetseka (squared exponential) kana rougher Matern kernel, inodzora kutsetseka uye kureba zviyero. Uku kusanganiswa kwekuchinjika uye kusavimbika kwechokwadi kunoita kuti maGPs ave akanaka kune madiki dataset uye kuyedza kunodhura.
Kuitwa muNyika yeChokwadi
Bayesian optimization ye tuning modhi hyperparameter ine miyedzo mishoma
Kuenzanisira uye kududzira data renzvimbo senge terrain kana mazinga ekusvibisa
Mamodheru anotungamira bvunzo dzinodhura dzesainzi kana engineering
Nguva-yakatevedzana kufanotaura uko kwakagadziriswa nguva dzekuvimba kunodiwa
Ramangwana reGaussian Maitiro
GPs inoramba iri injini kuseri kweBayesian optimization, iyo yakajairwa nzira yekugadzira muchina-wekudzidza hyperparameter uye kugadzira kuyedza nemazvo. Tsvagiridzo inoshanda inonangana nekukasira kwavo kuburikidza nekufungidzira kushoma vachishandisa inducing mapoinzi uye stochastic variational inference, uye kuburikidza nekwakadzika kernel kudzidza kunobatanidza neural feature extractors neGP kusagadzikana. Tarisira kushandiswa kuri kukura mumarobhoti, kuwanikwa kwesainzi, uye chero marongero ane kusavimbika uye kugona kwedata kunodarika saizi yedataset.
Kuzvishandisa
Nei zvichikosha
- Mutengo uye bhajeti. Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore.
- Sarudzo dzakajeka. Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete.
- Kudzora kwemhando yepamusoro. Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika mukugadzira.
Chenjera
- Kugadzirisa imwe bhenji kunogona kuvanza yakafara system kushaya simba.
- Infrastructure uye mari yekugadzirisa inowanzotarisirwa pasi.
- Chengetedzo uye kucherechedzwa mapundu anogona kukura sezvo masisitimu anowedzera kuoma.
Matanho anotevera
- Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
- Benchmark pasi pechokwadi mutoro uye data mamiriro.
- Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro.
- Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera.
Ramba Uchiongorora
Tsamba yemahara
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Take the Gaussian Processes quiz
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Mibvunzo inowanzo bvunzwa
What is Gaussian Processes?
Gaussian process inzira inoshanduka, isiri parametric yekuenzanisira mabasa anouya neakavakirwa-mukati fungidziro yekusavimbika. Inokosheswa kana data iri kushomeka uye kuziva kuti ine chivimbo sei modhi yacho zvine basa sekufembera chaiko.
Gaussian process inotsanangura mukana wekugovera pamusoro pechii?
A GP inoisa kugovera pamusoro pemabasa ese, saka chero inogumira seti yemapoinzi yakabatana Gaussian.
Beyond a point estimate, what does a Gaussian Process return under its chosen kernel and noise assumptions?
A GP supplies a predictive distribution, but its uncertainty depends on the model assumptions and kernel.
Ko kernel (covariance function) inoita basa rei muGP?
Iyo kernel inotsanangura kuwirirana pakati pemapoinzi, kutonga zvivakwa senge kutsetseka uye kureba kwechiyero cheiyo modeled basa.
Sei akajairwa Gaussian Maitiro achinetsekana nemaseti makuru kwazvo?
Iyo chaiyo yekumashure inoda inversion yen-by-n covariance matrix, iyo inoyera cubically nehuwandu hwemapoinzi.
Ko GP hyperparameters seyeyero yehurefu inowanzosarudzwa sei?
Kukwirisa muganho mukana wekutengesa kurekodzera iyo data inopesana nemuenzaniso kuoma kuseta hyperparameter.
Ramba uchidzidza
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