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Understanding Deep Learning

CORDIS · observation · Publication date unknown

Understanding Deep Learning While extremely successful, deep learning (DL) still lacks a solid theoretical foundation. In the last 5 years the PI focused almost entirely on DL theory, yielding a strong publication record with 7 papers at NeurIPS (the leading ML conference), including 2 spotlights (top 3% of submitted papers) and one oral (top 1%), 2 papers at ICLR (the leading DL conference), and 1 paper at COLT (the leading ML theory conference). These results are amongst the first that break a 20 years hiatus in NN theory, thereby giving some hope for a solid deep learning theory. This includes 1) the first poly-time learnability result for non-trivial function class by SGD on NN, 2) the first such result with near optimal rate, 3) new methodology to bound the sample complexity of NN, that established the first sample complexity bound that is sublinear in the number of parameters, under norm constr

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recordType

award

status

SIGNED

region

EU

value

1499750

unit

EUR