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+holds+potential+in+building+human-aligned+and+interpretable+machine+learning+models.+Despite+its+encouraging+prospect%252C+formalization+and+theoretical+insights+into+this+crucial+task+are+still+lacking.+In+this+work%252C+we+formalize+concepts+as+discrete+latent+causal+variables+that+are+related+via+a+hierarchical+causal+model+that+encodes+different+abstraction+levels+of+concepts+embedded+in+high-dimensional+data+(e.g.%252C+a+dog+breed+and+its+eye+shapes+in+natural+images).+We+formulate+conditions+to+facilitate+the+identification+of+the+proposed+causal+model%252C+which+reveals+when+learning+such+concepts+from+unsupervised+data+is+possible.+Our+conditions+permit+complex+causal+hierarchical+structures+beyond+latent+trees+and+multi-level+directed+acyclic+graphs+in+prior+work+and+can+handle+high-dimensional%252C+continuous+observed+variables%252C+which+is+well-suited+for+unstructured+data+modalities+such+as+images.+We+substantiate+our+theoretical+claims+with+synthetic+data+experiments.+Further%252C+we+discuss+our+theory%27s+implications+for+understanding+the+underlying+mechanisms+of+latent+diffusion+models+and+provide+corresponding+empirical+evidence+for+our+theoretical+insights.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.0bcd2b9e77d525af455d9777e89db2ec%3frik%3dvZYm7o4hHjXlww%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Hierarchical+Learning+Models&simid=607999437005331608&FORM=IRPRST&ck=42C47CDECEC93BA3E51993609D79B4CF&selectedIndex=4&itb=0)
%252C+a+multi-layered+network+structure+architecture+that+facilitates+information+exchange+between+the+multi-modal+representations+and+knowledge+integration+across+different+patch+resolutions.+Furthermore%252C+we+leverage+large+language+models+(LLMs)+to+generate+detailed+technical+descriptions+of+nanomaterials+as+auxiliary+information+to+assist+in+the+downstream+task.+We+utilize+a+cross-modal+attention+mechanism+for+knowledge+fusion+across+cross-domain+representations(both+image-based+and+linguistic+insights)+to+predict+the+nanomaterial+category.+This+multi-faceted+approach+promises+a+more+comprehensive+and+accurate+representation+and+classification+of+micrographs+for+nanomaterial+identification.+Our+framework+outperforms+traditional+methods%252C+overcoming+challenges+posed+by+distributional+shifts%252C+and+facilitating+high-throughput+screening.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.c4c5a7bcf2a7988e1ad3170f45e64858%3frik%3dlKmZYXIT09JDlA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Hierarchical+Learning+Models&simid=608052346713175565&FORM=IRPRST&ck=3DC6EBC126B743D9E08F4A7D46266C72&selectedIndex=20&itb=0)
