Usoro Gaussian
Usoro Gaussian bụ ụzọ na-agbanwe agbanwe, nke na-abụghị parametric iji gosipụta ọrụ ndị na-abịa na atụmatụ ejighị n'aka arụnyere.
Nchịkọta
It is prized when data is scarce and knowing how confident the model is matters as much as the prediction itself.
Ime miri emi
Usoro Gaussian (GP) na-akọwapụta nkesa puru omume n'elu ọrụ kama ịkwado paramita edoziri. N'ụzọ nkịtị, isi ihe ọ bụla nwere njedebe ewepụtara site na GP na-esochi nkesa Gaussian (nkịtị). Ị kọwapụta ọrụ pụtara yana, n'ụzọ dị oke mkpa, ọrụ covariance ma ọ bụ kernel nke na-akọwapụta otu nsonaazụ ahụ kwesịrị ịdị maka ntinye dị nso. Ka emechara ọnọdụ na data hụrụ, GP na-alaghachi ọ bụghị naanị uru amụma na ebe ọhụrụ ọ bụla kama nkesa amụma zuru oke, na-enye oge ntụkwasị obi na ogologo oge nke na-agbasawanye anya na data ahụ. Nhọrọ kernel, dị ka ezigbo RBF (squared exponential) ma ọ bụ rougher Matern kernel, na-achịkwa ire ụtọ na akpịrịkpa ogologo. Ngwakọta mgbanwe a na ejighị n'aka n'eziokwu na-eme ka ndị GP dị mma maka obere dataset na nnwale dị oke ọnụ.
Nghọta nka nka
Amụma na-ebelata ka ọ bụrụ algebra linear na kernel matrix: ihe dị n'azụ na ọdịiche na-abịa site na ịtụgharị n-by-n matrix covariance nke e wuru site na ntinye ọzụzụ. Mgbanwe ahụ na-akwụ ụgwọ n'usoro oge n-cubed, nke na-egbochi ndị GP enweghị isi na puku isi ole na ole. A na-enyocha ihe ngosi hyperparameters dị ka ogologo ogologo na ọkwa mkpọtụ site na ịbawanye ohere dị n'akụkụ, nke na-eme ka data dabara adaba megide mgbagwoju anya ụdị.
Mmetụta atụmatụ
Ọnụ ego na mmefu ego
Mkpebi ihe owuwu ụlọ na-akwalite arụmọrụ yana ọnụ ahịa ọrụ ruo ọtụtụ afọ.
Mkpebi doro anya
Nkà mmụta nka na-enyere ndị otu egwuregwu aka ịhọrọ nchịkọta ziri ezi, ọ bụghị naanị nke kachasị ọhụrụ.
Quality akara
Nhọrọ injinia ka mma na-ebelata ihe omume ntụkwasị obi na mmepụta.
Ọdịnihu nke usoro Gaussian
Ndị GP ka bụ injin dị n'azụ njikarịcha Bayesian, usoro ọkọlọtọ maka imezi hyperparameters mmụta igwe na imepụta nnwale nke ọma. Nchọpụta na-arụsi ọrụ ike na-elekwasị anya n'ịkwalite ha site na obere mkpirisi na-eji isi ihe na stochastic variational inference, yana site na mmụta kernel miri emi nke na-ejikọta ndị na-ewepụta ihe nhụsianya na GP na-ejighị n'aka. Na-atụ anya ka ojiji na-eto eto na robotics, nchọpụta sayensị, yana ntọala ọ bụla ebe ejighị n'aka na arụmọrụ data karịrị nha nke ngwa data.
Mmejuputa n'ezie n'ụwa
Nkwalite Bayesian maka nlegharị anya ụdị hyperparameters nwere nnwale ole na ole
Ịmepụta na ntinye data gbasara ohere dị ka ala ma ọ bụ ọkwa mmetọ
Ụdị ngbanwe nke na-eduzi nnwale sayensị ma ọ bụ injinia dị oke ọnụ
Ịma amụma usoro oge ebe achọrọ ogologo oge ntụkwasị obi
Ihe ize ndụ & okporo ụzọ nche
Ịkwalite otu akara ngosi nwere ike zoo adịghị ike sistemụ sara mbara.
A na-eledakarị ihe akụrụngwa na ụgwọ ọrụ anya.
Ọdịiche nchekwa na nleba anya nwere ike itolite ka sistemu na-adịwanye mgbagwoju anya.
Map mmejuputa
Kọwaa latency, ịdịmma na ebumnuche ọnụ ahịa tupu mmejuputa ya.
Benchmark n'okpuru ibu dị adị na ọnọdụ data.
Nleba anya akụrụngwa maka mperi, ịkpafu na mmetụta onye ọrụ.
Kwadebe ụzọ nzaghachi azụghachi azụ na ihe omume tupu ịchachaa.
Nọgide na-eme nchọpụta
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Ntuziaka na-esote
Gaussian Splatting
Ajụjụ a na-ajụkarị
What is Gaussian Processes?
Usoro Gaussian bụ ụzọ na-agbanwe agbanwe, nke na-abụghị parametric iji gosipụta ọrụ ndị na-abịa na atụmatụ ejighị n'aka arụnyere. Ọ na-adọrọ mmasị mgbe data dị ụkọ na ịmara otú obi ike ihe nlereanya ahụ si dị mkpa dị ka amụma ahụ n'onwe ya.
Usoro Gaussian na-akọwa nkesa puru omume na gịnị?
GP na-edobe nkesa n'elu ọrụ niile, ya mere, isi ihe ọ bụla nwere njedebe bụ Gaussian.
Kedu uru bụ isi nke GP na-enye karịa amụma amụma?
Ndị GP na-eweghachi nkesa amụma zuru oke, yabụ ị ga-enweta oge ntụkwasị obi na-agbasa ebe data dị ụkọ.
Kedu ọrụ kernel (ọrụ covariance) na-arụ na GP?
The kernel na-akọwa mmekọrịta dị n'etiti isi ihe, na-achịkwa akụrụngwa dị ka ire ụtọ na ogologo ogologo nke ọrụ egosipụtara.
Kedu ihe kpatara usoro Gaussian ọkọlọtọ ji na-agba mgba na nnukwu datasets?
Kpọmkwem nke dị n'azụ chọrọ ntụgharị nke matriks covariance n-by-n, nke na-eji ọnụ ọgụgụ isi ihe tụọ ya.
Kedu otu esi ahọrọ hyperparameters GP dị ka ogologo ogologo?
Ịbawanye ohere dị n'akụkụ na-eme ka ọ dabara na data megide mgbagwoju anya nlereanya iji tọọ hyperparameters.