Very recently, the unexpected combination of data structures and machine learning has led
to the development of a new area of research, called learned data structures. Their …

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The development of platforms and techniques for emerging Big Data and Machine Learning
applications requires the availability of real‐life datasets. A possible solution is to synthesize …

We review, for a general audience, a variety of recent experiments on extracting structure
from machine-learning mathematical data that have been compiled over the years. Focusing …

A serious problem in learning probabilistic models is the presence of hid (cid: 173) den
variables. These variables are not observed, yet interact with several of the observed …

For more than a century, the methods for data representation and the exploration of the
intrinsic structures of data have developed remarkably and consist of supervised and …

Organizations are collecting increasingly large amounts of data for data driven decision
making. These data are often dumped into a centralized repository, eg, a data lake …

In this thesis, we propose novel approaches to machine learning with structured data. Our
proposed methods are largely based on the theme of structuring the representations and …

We propose a solution based on first principles and AI to the decades-old problem of data
structure design. Instead of working on individual designs that each can only be helpful in a …

Abstract ENGLISH SUMMARY: From about 2006, deep learning has proven to be very
successul in application areas such as computer vision, natural language processing …