---
res:
bibo_abstract:
- "Deep learning is best known for its empirical success across a wide range of
applications\r\nspanning computer vision, natural language processing and speech.
Of equal significance,\r\nthough perhaps less known, are its ramifications for
learning theory: deep networks have\r\nbeen observed to perform surprisingly well
in the high-capacity regime, aka the overfitting\r\nor underspecified regime.
Classically, this regime on the far right of the bias-variance curve\r\nis associated
with poor generalisation; however, recent experiments with deep networks\r\nchallenge
this view.\r\n\r\nThis thesis is devoted to investigating various aspects of underspecification
in deep learning.\r\nFirst, we argue that deep learning models are underspecified
on two levels: a) any given\r\ntraining dataset can be fit by many different functions,
and b) any given function can be\r\nexpressed by many different parameter configurations.
We refer to the second kind of\r\nunderspecification as parameterisation redundancy
and we precisely characterise its extent.\r\nSecond, we characterise the implicit
criteria (the inductive bias) that guide learning in the\r\nunderspecified regime.
Specifically, we consider a nonlinear but tractable classification\r\nsetting,
and show that given the choice, neural networks learn classifiers with a large
margin.\r\nThird, we consider learning scenarios where the inductive bias is not
by itself sufficient to\r\ndeal with underspecification. We then study different
ways of â€˜tightening the specificationâ€™: i)\r\nIn the setting of representation
learning with variational autoencoders, we propose a hand-\r\ncrafted regulariser
based on mutual information. ii) In the setting of binary classification, we\r\nconsider
soft-label (real-valued) supervision. We derive a generalisation bound for linear\r\nnetworks
supervised in this way and verify that soft labels facilitate fast learning. Finally,
we\r\nexplore an application of soft-label supervision to the training of multi-exit
models.@eng"
bibo_authorlist:
- foaf_Person:
foaf_givenName: Phuong
foaf_name: Bui Thi Mai, Phuong
foaf_surname: Bui Thi Mai
foaf_workInfoHomepage: http://www.librecat.org/personId=3EC6EE64-F248-11E8-B48F-1D18A9856A87
bibo_doi: 10.15479/AT:ISTA:9418
dct_date: 2021^xs_gYear
dct_isPartOf:
- http://id.crossref.org/issn/2663-337X
dct_language: eng
dct_publisher: Institute of Science and Technology Austria@
dct_title: Underspecification in deep learning@
...