Ñaareelu jaar-jaar bi gëna xéewale ak njuréefi Newton
Ñaareelu rang bi gëna xéewale dafay jëfandikoo leerali courbure (matrix Hessien bu ñaareelu derivatif) ngir jël jéego yu gëna am xel ngir yegg ci lu gëna ndaw, te baña yam ci pente bi kese.
Résumé
It can converge in dramatically fewer iterations than plain gradient descent, but the cost of computing curvature makes it tricky to scale.
Plongeur bu xóot
Wàccig gradient bi xamna pente bi ci sa poñ bi fi nekk, moo tax dafay tànn dayo jéego bu fiks wala buñ defaree loxo, ba noppi yaakaar li gëna baax. Xeetu Newton dafa dem lu gëna sori: dafay xool itam ni pente bi di soppeekoo (courbure bi), mu jàpp ci Hessian bi, di matrix bu am ñaareelu derivatif partiel yépp. Coppite bi dafay yokk Hessian bu dëddu bi ak degrade bi, biy soppi ci saasi yoon wu nekk ba noppi wàcci ci wetu li gëna ndaw ci xayma kwadratik bi ci gox bi. Ngir bool bu mat sëkk, njuréefi Newton dafay dem ba ci suufa suuf ci benn jéego. Japp bi mettiwoon na: ab model bu am N parametre amna Hessian N-by-N, kon denc ko ak soppi ko lu tollu ci N-mémoire carré ak N-cubed calcul. Ngir reso yu am ay miliyaar ciy parametre loolu mënatul am, moo tax praktisien yi di jëfandikoo xayma yu yomb.
Gis-gis xarala
Coppite Newton bi mooy x_beesu = x - H_ñu dellu ginaaw ci penku bi, fu H nekk Hessian bi. Pexem Quasi-Newton yu melni BFGS ak L-BFGS moytu nañu xayma H ci saasi ci tabax ap xayma buy daw ci inverseem ci wuute gradient yu toppalante. L-BFGS du denc yenn vecteur gradient yu mujj yi ak jéego yi ci barabu matrix bi yépp, dagg mémoire bi ci N-carré dem ba ci N bu ndaw, boole ci tëye li gëna bari ci gaawaayu convergence bi.
njeextalu pexe
Njëgg ak budget
Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw.
dogal yu gëna leer
Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal.
Xool kalite
Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi.
Ëlëgu ñaareelu yoon ci gëna xéewale ak Newton
Ngir reso neuronal yu mag yi, pexe yu mat yi ci ñaareelu rang nekkul luñu mëna jëfandikoo, waaye xayma yi dañu gëna am solo. Optimiser yu melni K-FAC ak Shampoo dañuy xayma courbure bi ci diagonale block wala facteur Kronecker, ak pexe yu bees yu melni Sophia ak Muon ñuy jëfandikoo xayma courbure bu yomb ngir gaawlu tàggat modelu làkk bu yaatu. Xaarandil coono buy wéy ngir jàpp siñaalu courbure bu am njariñ ci njëg bu jege-first-ordre, wàññi gap bi am ci digganté Adam ak jéego yu dëggu yu Newton.
Doxal ci àdduna dëgg
L-BFGS méngoo ak régression logistic ak yeneen xeetu convex ci scikit-learn, fu muy faral di raw wàccinu gradient bu leer ci kaw done yu ndaw wala yu digg
Defar ay mbir ci tabaxaat 3D ak SLAM, fu Gauss-Newton ak Levenberg-Marquardt setal pose yi ak poñ yi
Taggat ay reso neuronal yu ndaw yu am xibaar ci physique, fu L-BFGS mëna am njubte gi Adam di jéema yegg
Shampoo ak K-FAC di gaawlu tàggat yaram bu xóot ci anam wu yaatu ci jegewaale jumtukaayu Hessian
Risk yi ak balustrade yi
Optimize benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi.
Njëg li ñuy fay ci infrastructure yi ak ci toppatoo dañuy faral di suufeel.
Bu sistem yi di gëna xawa jafee xam, jafe-jafe yi am ci wàllu kaaraange ak seetlu mën nañu gëna bari.
Roadmap ngir samp gi
Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo.
Benchmark ci biir sargal ak done yu dëggu.
Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi.
Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale.
Weyal di banneexu
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Gis bi ci topp
Groupe bi gëna xéewale ci wàllu politik
Laaj yi ñuy faral di laaj
What is Second-Order Optimization and Newton Methods?
Ñaareelu rang bi gëna xéewale dafay jëfandikoo leerali courbure (matrix Hessien bu ñaareelu derivatif) ngir jël jéego yu gëna am xel ngir yegg ci lu gëna ndaw, te baña yam ci pente bi kese. Mën na booloo ci ay iteration yu néew lool ci wàcci gradient bu leer, waaye njëgu xayma courbure bi taxna mu jafe ci escaleer.
Ban leeral la njuréefi Newton jëfandikoo te wàccinu gradient bu leer jëfandikoowul?
Pexem Newton dafay yokk degrade bi ak courbure bu bawoo ci Hessian, loolu may ko mu soppi yoon wi mu jëm, ba noppi jegesi minimum quadratique bi ci gox bi.
Ngir am objectif quadratique bu mat sëkk, ñaata jéego la méthode bu Newton wara def ngir mëna yegg ci li gëna ndaw?
Ci kaw benn quadratic bu dëggu, modelu quadratic local bi dafay méngoo ak fonction dëgg bi, kon benn jéego bu Newton dafay tëb jubal ci bi gëna ndaw.
Lan moo waral pexem Newton bi amul njariñ ci reso neuronal yu am ay miliyaar ciy parametre?
Ak N parametre Hessian amna duggukaay N-kaare ak soppi ko ci balans yu melni N-cubed, te loolu mënul am ci ay miliyaar ci parametre yi.
Lan la pexe quasi-Newton yu melni BFGS di def ngir moytu njëgu Hessian?
BFGS dafay yeesal xayma Hessian bu dëddu bi ci jëfandikoo coppite yi ci degrade bi diggante jéego yi, moytu xayma bu jub.
naka la L-BFGS di wàññi memory buñu ko méngale ak BFGS?
'L' dafay tekki memory bu gàtt: L-BFGS dafay denc ay vecteur yu bees yu néew, wàññi dencukaay bi ci N-carré ba ci lu tollu ci N.