{"id":30429,"date":"2023-12-20T12:52:53","date_gmt":"2023-12-20T11:52:53","guid":{"rendered":"https:\/\/kinit.sk\/research-visit-at-the-eindhoven-university-of-technology\/"},"modified":"2023-12-20T12:57:08","modified_gmt":"2023-12-20T11:57:08","slug":"vyskumna-navsteva-na-technickej-univerzite-v-eindhovene","status":"publish","type":"post","link":"https:\/\/kinit.sk\/sk\/vyskumna-navsteva-na-technickej-univerzite-v-eindhovene\/","title":{"rendered":"V\u00fdskumn\u00e1 n\u00e1v\u0161teva na Technickej univerzite v Eindhovene\u00a0"},"content":{"rendered":"
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Branislav Pecher<\/em><\/a> je jeden z na\u0161ich doktorandov. Ako \u010dlen t\u00edmu <\/em>WUDAP<\/em><\/a> sa zameriava na u\u010diace sa modely, ktor\u00e9 zah\u0155\u0148aj\u00fa iba obmedzen\u00fd po\u010det anotovan\u00fdch vzoriek. Venuje sa predov\u0161etk\u00fdm u\u010deniu s kontextom (angl. In-context learning), prenosov\u00e9mu u\u010deniu s prenosom a meta-u\u010deniu. V auguste 2023 dostal Branislav v\u00fdnimo\u010dn\u00fa pr\u00edle\u017eitos\u0165 absolvova\u0165 v r\u00e1mci\u00a0 <\/em>projektu TAILOR<\/em><\/a> v\u00fdskumn\u00fa n\u00e1v\u0161tevu. \u010c\u00edtajte \u010falej a dozviete sa viac o jeho sk\u00fasenostiach na <\/em>Technickej univerzite v Eindhovene<\/em><\/a> a o projekte, na ktorom tam pracoval.<\/em><\/p>\n<\/div>\n\n

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Aktu\u00e1lna situ\u00e1cia v oblasti hlbok\u00e9ho u\u010denia vyzer\u00e1 tak, \u017ee na tr\u00e9novanie ve\u013ek\u00fdch neur\u00f3nov\u00fdch siet\u00ed na pou\u017eitie na konkr\u00e9tnu \u00falohu sa vyu\u017e\u00edvaj\u00fa obrovsk\u00e9 datasety. Aj ke\u010f vyu\u017eitie ve\u013ek\u00e9ho datasetu vedie k modelom s vysokou \u00faspe\u0161nos\u0165ou, vyskytuj\u00fa sa tam dva hlavn\u00e9 probl\u00e9my:<\/p>\n<\/div>\n\n

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1) Pre mnoh\u00e9 oblasti, ako napr\u00edklad detekcia dezinform\u00e1ci\u00ed, predstavuje z\u00edskanie dostato\u010dn\u00e9ho mno\u017estva ozna\u010den\u00fdch d\u00e1t ve\u013ek\u00fa v\u00fdzvu.<\/p>\n<\/div>\n\n

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2) Ned\u00e1vne \u0161t\u00fadie uk\u00e1zali, \u017ee kvalita vzoriek v datasetoch je \u010dasto d\u00f4le\u017eitej\u0161ia ne\u017e ich mno\u017estvo, ke\u010f\u017ee existuje mno\u017estvo vzoriek n\u00edzkej kvality, ktor\u00e9 bu\u010f nepon\u00fakaj\u00fa \u017eiadne inform\u00e1cie alebo poskytuj\u00fa inform\u00e1cie, ktor\u00e9 s\u00fa zav\u00e1dzaj\u00face pri tr\u00e9novan\u00ed modelu. Preto je d\u00f4le\u017eit\u00e9, aby boli tieto vzorky adekv\u00e1tne filtrovan\u00e9. Ich pr\u00edtomnos\u0165 bez spr\u00e1vneho filtrovania m\u00f4\u017ee negat\u00edvne ovplyvni\u0165 \u00faspe\u0161nos\u0165 modelu. <\/p>\n<\/div>\n\n

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Ke\u010f\u017ee ma t\u00e9ma efekt\u00edvneho u\u010denia s obmedzen\u00fdm mno\u017estvom ozna\u010den\u00fdch \u00fadajov fascinuje, prepojenie t\u00fdchto dvoch probl\u00e9mov je pre m\u0148a ve\u013emi zauj\u00edmav\u00e9. Ak\u00fd vplyv m\u00e1 kvalita \u00fadajov na v\u00fdkonnos\u0165 pri pr\u00e1ci s obmedzen\u00fdm mno\u017estvom ozna\u010den\u00fdch \u00fadajov?<\/strong><\/p>\n<\/div>\n\n

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Tento v\u00fdskumn\u00fd probl\u00e9m bol hlavnou t\u00e9mou projektu, na ktorom som pracoval po\u010das mojej v\u00fdskumnej n\u00e1v\u0161tevy na Technickej univerzite v Eindhovene. Po\u010das svojej n\u00e1v\u0161tevy som spolupracoval s t\u00edmom Automated Machine Learning<\/a> pod veden\u00edm Joaquina Vanschorena<\/a>. Joaquin je jedn\u00fdm z ved\u00facich odborn\u00edkov v oblasti efekt\u00edvneho a automatizovan\u00e9ho strojov\u00e9ho u\u010denia. T\u00e1to oblas\u0165 zah\u0155\u0148a t\u00e9my ako kontinu\u00e1lne u\u010denie, u\u010denie s prenosom a meta-u\u010denie. V\u00fdskumn\u00e1 n\u00e1v\u0161teva bola realizovan\u00e1 s finan\u010dnou podporou TAILOR Collaboration Exchange Fund.<\/a><\/p>\n<\/div>\n\n

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Budova MetaForum na Technickej univerzite v Eindhovene, v ktorej s\u00eddli Automated Machine Learning group. Tu som str\u00e1vil 3 mesiace.<\/em><\/figcaption><\/figure>\n<\/div>\n\n
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Projekt v\u00fdskumnej n\u00e1v\u0161tevy: Efekt\u00edvny v\u00fdber vzoriek<\/h3>\n<\/div>\n\n
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Ako prv\u00fd krok n\u00e1\u0161ho projektu sme analyzovali u\u017e existuj\u00face pr\u00e1ce zameran\u00e9 na v\u00fdber vzoriek v r\u00f4znych oblastiach. T\u00fdmtom v\u00fdskumn\u00fdm probl\u00e9mom sa zaober\u00e1 mno\u017estvo v\u00fdskumn\u00edkov, ke\u010f\u017ee efekt\u00edvne u\u010denie s vyu\u017eit\u00edm mal\u00e9ho po\u010dtu vzoriek je nie\u010do, o \u010do sa v\u00fdskum v umelej inteligencii dlhodobo sna\u017e\u00ed. <\/p>\n<\/div>\n\n

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V tejto oblasti u\u017e pozn\u00e1me mnoho strat\u00e9gi\u00ed v\u00fdberu vzoriek:<\/p>\n<\/div>\n\n

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1) Strat\u00e9gie akt\u00edvneho u\u010denia<\/strong> vyberaj\u00fa vzorky z neozna\u010den\u00fdch \u00fadajov, ktor\u00e9 by mali by\u0165 n\u00e1sledne anotovan\u00e9, s cie\u013eom zn\u00ed\u017ei\u0165 za\u0165a\u017eenie anot\u00e1torov. Z\u00e1rove\u0148 poskytuj\u00fa najinformat\u00edvnej\u0161ie vzorky.<\/p>\n<\/div>\n\n

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2) Strat\u00e9gie v\u00fdberu z\u00e1kladnej mno\u017einy<\/strong> s\u00fa navrhnut\u00e9 na v\u00fdber reprezentat\u00edvnej podmno\u017einy v\u00e4\u010d\u0161ieho anotovan\u00e9ho datasetu, t.j. podmno\u017einy s podobn\u00fdmi charakteristikami, ktor\u00e9 ved\u00fa k podobn\u00fdm v\u00fdsledkom.<\/p>\n<\/div>\n\n

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3) Strat\u00e9gie zalo\u017een\u00e9 na kvalite\/heuristike<\/strong> maj\u00fa za cie\u013e vybera\u0165 vzorky najvy\u0161\u0161ej kvality na z\u00e1klade ur\u010dit\u00fdch heurist\u00edk (d\u013a\u017eka textu, obtia\u017enos\u0165 u\u010denia konkr\u00e9tnych vzoriek alebo pr\u00ednos v\u00fdkonnosti konkr\u00e9tnej vzorky).<\/p>\n<\/div>\n\n

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Hoci existuje mnoho r\u00f4znych strat\u00e9gi\u00ed v\u00fdberu vzoriek, ich pr\u00ednos bol doteraz hodnoten\u00fd len v jednoduch\u0161\u00edch nastaveniach klasifik\u00e1cie. Z tohto d\u00f4vodu zatia\u013e nie je pr\u00ednos pre u\u010denie s mal\u00fdm po\u010dtom vzoriek,<\/strong> kde sa mus\u00ed natr\u00e9novan\u00fd model prisp\u00f4sobi\u0165 predt\u00fdm neviden\u00fdm \u00faloh\u00e1m a triedam, dostato\u010dne presk\u00faman\u00fd<\/strong>.<\/p>\n<\/div>\n\n

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Na z\u00e1klade zisten\u00ed na\u0161ej anal\u00fdzy sme sa rozhodli presk\u00fama\u0165 a vyhodnoti\u0165 pr\u00ednos podmno\u017einy najlep\u0161\u00edch strat\u00e9gi\u00ed v\u00fdberu vzoriek v nastaven\u00ed u\u010denia s mal\u00fdm po\u010dtom vzoriek. Vyu\u017e\u00edvali sme pritom pr\u00edstupy meta-u\u010denia a u\u010denia s prenosom s  mal\u00fdm po\u010dtom vzoriek. N\u00e1sledne sme ich porovnali s v\u00fdkonom jednoduch\u00e9ho referen\u010dn\u00e9ho modelu, v ktorom boli vzorky vybran\u00e9 n\u00e1hodne. Strat\u00e9gie v\u00fdberu vzoriek uk\u00e1zali, \u017ee ich pomocou je mo\u017en\u00e9 zv\u00fd\u0161i\u0165 celkov\u00fd v\u00fdkon adapt\u00e1cie na \u00faplne nov\u00e9 \u00falohy v priemere o 2%. <\/strong>Av\u0161ak strat\u00e9gie v\u00fdberu vzoriek uk\u00e1zali aj v\u00fdrazn\u00fa z\u00e1vislos\u0165 od datasetu a modelu, pri\u010dom najv\u00fdhodnej\u0161ia strat\u00e9gia bola odli\u0161n\u00e1 v r\u00e1mci sk\u00faman\u00fdch modelov a datasetov. Okrem toho bola sk\u00faman\u00e1 len jedna strat\u00e9gia v jednom \u010dase. Z tohto d\u00f4vodu sme nemohli sk\u00fama\u0165 v\u00fdhodu ich kombin\u00e1cie (a to ani v predch\u00e1dzaj\u00facich \u0161t\u00fadi\u00e1ch).<\/strong><\/p>\n<\/div>\n\n

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To otv\u00e1ra \u010fal\u0161iu v\u00fdskumn\u00fa ot\u00e1zku a probl\u00e9m – poskytuje kombin\u00e1cia r\u00f4znych strat\u00e9gi\u00ed v\u00fdberu vzoriek v\u00e4\u010d\u0161ie v\u00fdhody a d\u00e1 sa t\u00e1to kombin\u00e1cia efekt\u00edvne a \u00fa\u010dinne n\u00e1js\u0165?<\/strong> Kompletn\u00e9 presk\u00famanie v\u0161etk\u00fdch mo\u017en\u00fdch kombin\u00e1ci\u00ed strat\u00e9gi\u00ed v\u00fdberu vzoriek by malo vies\u0165 k najvy\u0161\u0161iemu mo\u017en\u00e9mu pr\u00ednosu. Av\u0161ak to nie je mo\u017en\u00e9 vykona\u0165 pre ka\u017ed\u00fd dataset, ke\u010f\u017ee by si to vy\u017eadovalov v\u00fdrazn\u00e9 mno\u017estvo v\u00fdpo\u010dtov\u00e9ho \u010dasu.  <\/p>\n<\/div>\n\n

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Vizualiz\u00e1cia strat\u00e9gie v\u00fdberu kartografie, ktor\u00e1 ur\u010duje, ako jednoduch\u00e9, alebo obtia\u017ene je sa nau\u010di\u0165 r\u00f4zne vzorky na z\u00e1klade dynamiky tr\u00e9novania konkr\u00e9tnej vzorky (pozoruj\u00fac istotu modelu na danej vzorke a variabilitu tej istoty po\u010das r\u00f4znych tr\u00e9novac\u00edch epiz\u00f3d).<\/em><\/figcaption><\/figure>\n<\/div>\n\n
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Ako druh\u00fd krok n\u00e1\u0161ho projektu sme sa preto rozhodli presk\u00fama\u0165, ako efekt\u00edvne kombinova\u0165 r\u00f4zne strat\u00e9gie. Navrhli sme tri samostatn\u00e9 strat\u00e9gie na presk\u00famanie r\u00f4znych kombin\u00e1ci\u00ed, in\u0161pirovan\u00e9 predch\u00e1dzaj\u00facimi pr\u00e1cami (napr. Chang a Jia, 2023<\/a>, Ilyas et al., 2022<\/a>, Vilar et al., 2023<\/a>). Dve z nich boli in\u0161pirovan\u00e9 strat\u00e9giami na v\u00fdber \u010d\u0155t (dopredn\u00e1 selekcia a sp\u00e4tn\u00e1 selekcia, angl. forward and backward selection), a jedna bola zalo\u017een\u00e1 na regularizovanej line\u00e1rnej regresii. V\u010faka t\u00fdmto strat\u00e9gi\u00e1m sme identifikovali kombin\u00e1ciu strat\u00e9gi\u00ed v\u00fdberu vzorky, ktor\u00e1 poskytuje najv\u00fdraznej\u0161\u00ed pr\u00ednos, a to naprie\u010d v\u0161etk\u00fdmi presk\u00faman\u00fdmi modelmi a d\u00e1tov\u00fdmi sadami \u2013 teda vytv\u00e1ra kombin\u00e1ciu, ktor\u00e1 nie je z\u00e1visl\u00e1 na \u017eiadnej d\u00e1tovej sade ani modeli.<\/p>\n<\/div>\n\n

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Vybra\u0165 vzorky, ktor\u00e9 sa nach\u00e1dzaj\u00fa na hranici medzi jednotliv\u00fdmi triedami (angl. Decision boundary) a s\u00fa jednoduch\u00e9 na nau\u010denie, prin\u00e1\u0161a teda najv\u00e4\u010d\u0161ie v\u00fdhody pri u\u010den\u00ed s mal\u00fdm mno\u017estvom pr\u00edkladov, ke\u010f je potrebn\u00e9 prisp\u00f4sobi\u0165 sa nov\u00fdm \u00faloh\u00e1m a triedam. <\/strong>Tak\u00e1to strat\u00e9gia m\u00f4\u017ee priemerne zv\u00fd\u0161i\u0165 v\u00fdkonnos\u0165 o \u010fal\u0161ie 1-2%.<\/p>\n<\/div>\n\n

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Tento z\u00e1ver je ve\u013emi zauj\u00edmav\u00fd a ver\u00edm, \u017ee si zasl\u00fa\u017ei \u010fal\u0161ie presk\u00famanie. Aj ke\u010f sa v\u00fdskumn\u00e1 n\u00e1v\u0161teva u\u017e skon\u010dila, v tomto projekte pokra\u010dujeme jeho roz\u0161\u00edren\u00edm na \u010fal\u0161ie d\u00e1tov\u00e9 sady – obrazov\u00e9 a textov\u00e9 datasety, aby sme mohli z\u00edska\u0165 v\u0161eobecnej\u0161ie z\u00e1very. Ke\u010f\u017ee v\u00fdber vzoriek sa uk\u00e1zal ako jeden z najv\u00fdznamnej\u0161\u00edch faktorov ovplyv\u0148uj\u00facich v\u00fdkonnos\u0165 a stabilitu r\u00f4znych modelov u\u010denia v kontexte (angl. in-context learning), \u010fal\u0161ou mo\u017enou t\u00e9mou do bud\u00facnosti je zameranie sa na v\u00fdber vzoriek v tomto type u\u010denia (ktor\u00e9 je v\u0161ak pou\u017eite\u013en\u00e9 len pre textov\u00e9 d\u00e1ta). <\/p>\n<\/div>\n\n

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Sk\u00fasenosti a pr\u00ednosy v\u00fdskumnej n\u00e1v\u0161tevy <\/h3>\n<\/div>\n\n
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Ako som u\u017e spom\u00ednal, projekt bol realizovan\u00fd v r\u00e1mci v\u00fdskumnej n\u00e1v\u0161tevy financovanej za podpory TAILOR Collaboration Exchange Fund<\/a>. Po\u010das svojho pobytu som spolupracoval s AutoML group<\/a> na Technickej univerzite v Eindhovene pod veden\u00edm profesora Joaquina Vanschorena.<\/a><\/p>\n<\/div>\n\n

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V\u00fdskumn\u00e1 n\u00e1v\u0161teva bola pre m\u0148a v\u00fdnimo\u010dnou a jedine\u010dnou pr\u00edle\u017eitos\u0165ou. Umo\u017enila mi rozobera\u0165 v\u00fdskumn\u00e9 t\u00e9my a probl\u00e9my s \u010fal\u0161\u00edmi v\u00fdskumn\u00edkmi, s ktor\u00fdmi sme okrem  zauj\u00edmav\u00fdch diskusi\u00ed o v\u00fdskumnom projekte rozoberali aj \u010fal\u0161ie koncepty v efekt\u00edvnom strojovom u\u010den\u00ed a hlbokom u\u010den\u00ed. Navy\u0161e mi t\u00e1to st\u00e1\u017e uk\u00e1zala, ako in\u00ed v\u00fdskumn\u00edci rie\u0161ia v\u00fdskumn\u00e9 probl\u00e9my, formuluj\u00fa v\u00fdskumn\u00e9 ot\u00e1zky a navrhuj\u00fa metodol\u00f3giu a experimenty.<\/p>\n<\/div>\n\n

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V neposlednom rade som si roz\u0161\u00edril m\u00f4j profesijn\u00fd okruh o \u0161pi\u010dkov\u00fdch v\u00fdskumn\u00edkov v mojej oblasti. Tieto nov\u00e9 kontakty m\u00f4\u017eu v bud\u00facnosti vies\u0165 k zauj\u00edmav\u00fdm spolupr\u00e1cam, ako napr\u00edklad pokra\u010dovaniu v pr\u00e1ci na projekte z v\u00fdskumnej n\u00e1v\u0161tevy. Ver\u00edm, \u017ee \u00fa\u010das\u0165 na konzult\u00e1ci\u00e1ch a prezent\u00e1ci\u00e1ch mi pomohla zlep\u0161i\u0165 moje v\u00fdskumn\u00edcke schopnosti.<\/strong> V\u0161etky sk\u00fasenosti z v\u00fdskumnej n\u00e1v\u0161tevy nepochybne prispeli k m\u00f4jmu profesion\u00e1lnemu rastu a posilnili moje znalosti v oblasti strojov\u00e9ho u\u010denia.<\/p>\n<\/div>\n\n

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Okrem toho, \u017ee som naviazal nov\u00e9 kontakty s v\u00fdskumn\u00edkmi pracuj\u00facimi v rovnakom odbore, na chv\u00ed\u013eu som sa stal aj s\u00fa\u010das\u0165ou Univerzity. Napr\u00edklad som sa z\u00fa\u010dastnil behu na 5 kilometrov v r\u00e1mci Eidenhovensk\u00e9ho marat\u00f3nu ako \u010dlen univerzitn\u00e9ho t\u00edmu .  <\/p>\n<\/div>\n\n

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T\u00edm Technickej univerzity v Eindhovene po dokon\u010den\u00ed 5 km behu<\/em><\/figcaption><\/figure>\n<\/div>\n\n
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Pobyt taktie\u017e prispel aj k m\u00f4jmu v\u00fdskumu. Projekt, na ktorom som pracoval je neoddelite\u013enou s\u00fa\u010das\u0165ou mojej dizerta\u010dnej t\u00e9my. Spolupr\u00e1ca s v\u00fdskumn\u00edkmi, bohat\u00e9 diskusie a sp\u00e4tn\u00e1 v\u00e4zba v\u00fdrazne prispeli k zdokonaleniu mojej dizerta\u010dnej pr\u00e1ce.<\/strong><\/p>\n<\/div>\n\n

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Hoci je v\u00fdskumn\u00e1 n\u00e1v\u0161teva u\u017e za mnou, st\u00e1le pracujeme na roz\u0161\u00edren\u00ed n\u00e1\u0161ho projektu s cie\u013eom zlep\u0161i\u0165 v\u0161eobecnos\u0165 jeho v\u00fdsledkov a zisten\u00ed. V\u00fdsledky pl\u00e1nujeme zhrn\u00fa\u0165 v spolo\u010dnom \u010dl\u00e1nku, ktor\u00fd sa chyst\u00e1me poda\u0165 na konferenciu ICML 2024<\/a>.<\/p>\n<\/div>\n\n

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Celkovo bola moja v\u00fdskumn\u00e1 n\u00e1v\u0161teva skvel\u00fdm z\u00e1\u017eitkom, ktor\u00fd mi priniesol mnoho pr\u00edle\u017eitost\u00ed pre profesion\u00e1lny aj osobn\u00fd rast. T\u00e1to st\u00e1\u017e mi poskytla mo\u017enos\u0165 rozv\u00edja\u0165 sa ako v\u00fdskumn\u00edk a pozit\u00edvne ovplyvnila aj moje doktorandsk\u00e9 \u0161t\u00fadium.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

Branislav Pecher je jeden z na\u0161ich doktorandov. Ako \u010dlen t\u00edmu WUDAP sa zameriava na u\u010diace sa modely, ktor\u00e9 zah\u0155\u0148aj\u00fa iba obmedzen\u00fd po\u010det anotovan\u00fdch vzoriek. Venuje sa predov\u0161etk\u00fdm u\u010deniu s kontextom…<\/p>\n","protected":false},"author":26,"featured_media":30409,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[83,142],"tags":[402],"class_list":["post-30429","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-sk","category-2023-sk","tag-machine-learning-sk"],"acf":[],"yoast_head":"\nV\u00fdskumn\u00e1 n\u00e1v\u0161teva na Technickej univerzite v Eindhovene\u00a0 - KInIT<\/title>\n<meta name=\"description\" content=\"Branislav Pecher je jeden z na\u0161ich doktorandov. Ako \u010dlen t\u00edmu WUDAP sa zameriava na u\u010diace sa modely, ktor\u00e9 zah\u0155\u0148aj\u00fa iba obmedzen\u00fd po\u010det anotovan\u00fdch vzoriek. Venuje sa predov\u0161etk\u00fdm u\u010deniu s kontextom (angl. In-context learning), prenosov\u00e9mu u\u010deniu s prenosom a meta-u\u010deniu. 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