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BEGIN:VEVENT
SUMMARY:Talk of Professor Taiji Suzuki (University of Tokyo)
DTSTART:20191101T120000
DTEND:20191101T140000
DTSTAMP:20260916T232044Z
UID:392cb142be70e58f85208ee50e36385e62ea16b8803571bfde3c7948
CATEGORIES:Conferences - Seminars
DESCRIPTION:Professor Taiji Suzuki\nTitle:\nGeneralization analysis and op
 timization of deep learning: adaptivity and kernel view\n\nAbstract: In th
 is talk\, I will discuss the adaptivity of deep learning\, and the general
 ization ability and optimization property under overparameterized settings
 . In the first half\, we theoretically show that deep learning can extract
  appropriate bases in an adaptive way and thus can achieve superior perfor
 mance than kernel methods especially on models with non-convexity property
 . Thanks to this properties\, deep learning can outperform kernel methods 
 if input data are high dimensional and the target functions are in Besov s
 pace.\nIn the later half\, we discuss the generalization ability and optim
 ization property of deep learning under overparameterized settings. The cl
 assical learning theory suggests that overparameterized models cause overf
 itting. However\, practically used large deep models avoid overfitting\, w
 hich is not well explained by the classical approaches. To resolve this is
 sue\, we give a new unified frame-work for deriving a compression based bo
 und. The existing compression based bounds can only be applied to a compre
 ssed network\, but our bound can convert those bounds to that of non-compr
 essed original network. Finally\, we discuss the optimization aspects of n
 eural networks under the neural tangent kernel regime. We show that for a 
 classification task\, the width of networks can be much smaller than exist
 ing studies to obtain a near global optimal solution by a gradient descent
 .\n\nBIO: Taiji Suzuki is currently an Associate Professor in the Departme
 nt of Mathematical Informatics at the University of Tokyo. He also serves 
 as the team leader of "deep learning theory group" in AIP-RIKEN. He receiv
 ed his Ph.D. degree in information science and technology from the Univers
 ity of Tokyo in 2009. He has a broad research interest in statistical lear
 ning theory on deep learning\, kernel methods and sparse estimation\, and 
 stochastic optimization for large-scale machine learning problems. He serv
 ed as technical program committee members of premier conferences such as N
 eurIPS\, ICML\, ICLR\, COLT\, AISTATS and ACML. He received Outstanding Ac
 hievement Award in 2017 from the Japan Statistical Society\, Outstanding A
 chievement Award in 2016 from the Japan Society for Industrial and Applied
  Mathematics\, and Best Paper Award in 2012 from IBISML. 
LOCATION:INM 10 https://plan.epfl.ch/?room==INM%2010
STATUS:CONFIRMED
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