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@article{achille2017emergence,
author = {Achille, Alessandro and Soatto, Stefano},
publisher = {JMLR.org},
date = {2018-01},
issn = {1532-4435},
journaltitle = {J. Mach. Learn. Res.},
number = {1},
pages = {1947--1980},
title = {Emergence of Invariance and Disentangling in Deep Representations},
volume = {19}
}
@misc{achille2018critical,
author = {Achille, Alessandro and Rovere, Matteo and Soatto, Stefano},
date = {2017},
eprint = {1711.08856},
eprintclass = {cs.LG},
eprinttype = {arXiv},
title = {Critical Learning Periods in Deep Neural Networks}
}
@misc{achille2018dynamics,
author = {Achille, Alessandro and Mbeng, Glen and Soatto, Stefano},
date = {2018},
eprint = {1810.02440},
eprintclass = {cs.LG},
eprinttype = {arXiv},
title = {Dynamics and Reachability of Learning Tasks}
}
@article{achille2018infodropout,
author = {Achille, Alessandro and Soatto, Stefano},
publisher = {IEEE},
date = {2018},
journaltitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
number = {12},
pages = {2897--2905},
title = {Information Dropout: Learning Optimal Representations Through Noisy Computation},
volume = {40}
}
@misc{achille2019complexity,
author = {Achille, Alessandro and Paolini, Giovanni and Mbeng, Glen and Soatto, Stefano},
date = {2019},
eprint = {1904.03292},
eprintclass = {cs.LG},
eprinttype = {arXiv},
title = {The Information Complexity of Learning Tasks, their Structure and their Distance},
volume = {abs/1904.03292}
}
@thesis{achille2019phd,
author = {Achille, Alessandro},
institution = {UCLA},
url = {https://escholarship.org/uc/item/8gb8x6w9},
date = {2019},
title = {Emergent Properties of Deep Neural Networks},
type = {phdthesis}
}
@inproceedings{achille2019task2vec,
author = {Achille, Alessandro and Lam, Michael and Tewari, Rahul and Ravichandran, Avinash and Maji, Subhransu and Fowlkes, Charless and Soatto, Stefano and Perona, Pietro},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
date = {2019-10},
title = {Task2Vec: Task Embedding for Meta-Learning}
}
@misc{achille2019tutorial,
author = {Achille, Alessandro},
howpublished = {\url{https://alexachi.github.io/cs103/index.html}},
url = {https://alexachi.github.io/cs103/index.html},
date = {2019},
note = {[Online; accessed on June 20th, 2019]},
title = {CS103 ‐ Topics in Representation Learning, Information Theory and Control}
}
@misc{achille2019weights,
author = {Achille, Alessandro and Soatto, Stefano},
date = {2019},
eprint = {1905.12213},
eprintclass = {cs.LG},
eprinttype = {arXiv},
title = {Where is the Information in a Deep Neural Network?}
}
@misc{aftab2001,
author = {Aftab, O. and Cheung, A. Kim and Thakkar, S. and Yeddanapudi, N.},
url = {http:https://web.mit.edu/6.933/www/Fall2001/Shannon2.pdf},
date = {2001},
howpublished = {Web document for 6.933 Project History, Massachusetts Institute of Technology.},
pages = {27},
title = {Information Theory: Information Theory and the Digital Age}
}
@article{alain2016,
title = {Understanding intermediate layers using linear classifier probes},
author = {Alain, Guillaume and Bengio, Yoshua},
journal = {arXiv preprint arXiv1610.01644},
date = {2016}
}
@article{alemi2016,
title = {Deep variational information bottleneck},
author = {Alemi, Alexander A and Fischer, Ian and Dillon, Joshua V and Murphy, Kevin},
journal = {arXiv preprint arXiv1612.00410},
year = {2016}
}
@book{aristotle2000,
author = {Aristotle},
publisher = {Cambridge University Press},
date = {2000},
doi = {10.1017/CBO9780511802058},
series = {Cambridge Texts in the History of Philosophy},
title = {Aristotle: Nicomachean Ethics}
}
@inproceedings{bachrach2003,
address = {Berlin, Heidelberg},
author = {Gilad-Bachrach, Ran and Navot, Amir and Tishby, Naftali},
booktitle = {Learning Theory and Kernel Machines},
editor = {Sch{\"o}lkopf, Bernhard and Warmuth, Manfred K.},
isbn = {978-3-540-45167-9},
pages = {595--609},
publisher = {Springer Berlin Heidelberg},
title = {An Information Theoretic Tradeoff between Complexity and Accuracy},
year = {2003}
}
@article{baxter2000,
author = {Baxter, Jonathan},
date = {2000},
journaltitle = {Journal of artificial intelligence research},
pages = {149--198},
title = {A model of inductive bias learning},
volume = {12}
}
@inproceedings{bengio2012,
author = {Bengio, Yoshua},
booktitle = {Proceedings of ICML workshop on unsupervised and transfer learning},
date = {2012},
pages = {17--36},
title = {Deep learning of representations for unsupervised and transfer learning}
}
@article{bialek2001,
author = {Bialek, William and Nemenman, Ilya and Tishby, Naftali},
publisher = {MIT Press},
date = {2001},
number = {11},
pages = {2409--2463},
title = {Predictability, complexity, and learning},
volume = {13}
}
@report{blum2007,
author = {Blum, Avrim},
institution = {Carnegie Mellon University, School of Computer Science},
url = {https://www.cs.cmu.edu/~avrim/Talks/Talks/mlt.pdf},
date = {2007},
title = {Machine learning theory},
type = {techreport}
}
@misc{caticha2008,
author = {Caticha, Ariel},
eprint = {0808.0012},
year = {2008},
date = {2008},
eprintclass = {physics.data-an},
eprinttype = {arXiv},
title = {Lectures on {{Probability}}, {{Entropy}}, and {{Statistical} Physics}}
}
@book{chaitin2006,
author = {Chaitin, Gregory},
title = {Meta Math! The Quest for Omega},
year = {2006},
isbn = {1400077974},
publisher = {Vintage Books}
}
@inproceedings{chaudhari2018SGD,
author = {P. {Chaudhari} and S. {Soatto}},
booktitle = {2018 Information Theory and Applications Workshop (ITA)},
title = {Stochastic Gradient Descent Performs Variational Inference, Converges to Limit Cycles for Deep Networks},
year = {2018},
pages = {1-10},
doi = {10.1109/ITA.2018.8503224}
}
@article{chaudhari2019,
title = {Entropy-sgd: Biasing gradient descent into wide valleys},
author = {Chaudhari, Pratik and Choromanska, Anna and Soatto, Stefano and LeCun, Yann and Baldassi, Carlo and Borgs, Christian and Chayes, Jennifer and Sagun, Levent and Zecchina, Riccardo},
journal = {Journal of Statistical Mechanics: Theory and Experiment},
volume = {2019},
number = {12},
year = {2019},
publisher = {IOP Publishing}
}
@inproceedings{chaudhari2019iclr,
title = {Entropy-SGD: Biasing gradient descent into wide valleys},
author = {Chaudhari, Pratik and Choromanska, Anna and Soatto, Stefano and LeCun, Yann and Baldassi, Carlo and Borgs, Christian and Chayes, Jennifer and Sagun, Levent and Zecchina, Riccardo},
booktitle = {5th International Conference on Learning Representations, ICLR 2017},
year = {2019}
}
@article{chaudhari2019entropy,
title = {Entropy-sgd: Biasing gradient descent into wide valleys},
author = {Chaudhari, Pratik and Choromanska, Anna and Soatto, Stefano and LeCun, Yann and Baldassi, Carlo and Borgs, Christian and Chayes, Jennifer and Sagun, Levent and Zecchina, Riccardo},
journal = {Journal of Statistical Mechanics: Theory and Experiment},
volume = {2019},
number = {12},
pages = {124018},
year = {2019},
publisher = {IOP Publishing}
}
@inproceedings{chelombiev2018,
title = {Adaptive Estimators Show Information Compression in Deep Neural Networks},
author = {Ivan Chelombiev and Conor Houghton and Cian O'Donnell},
booktitle = {International Conference on Learning Representations},
year = {2019}
}
@book{cover2006,
author = {Cover, T. M. and Thomas, Joy A.},
publisher = {Wiley-Interscience},
date = {2006},
edition = {2nd ed},
isbn = {9780-471-2419-5-9},
note = {OCLC: ocm59879802},
title = {Elements of Information Theory}
}
@incollection{csurka2017,
author = {Csurka, Gabriela},
publisher = {Springer International Publishing},
booktitle = {Domain Adaptation in Computer Vision Applications},
date = {2017},
doi = {10.1007/978-3-319-58347-1_1},
pages = {1--35},
title = {A Comprehensive Survey on Domain Adaptation for Visual Applications}
}
@inproceedings{dennett2009,
author = {Dennett, Daniel},
publisher = {National Academy of Sciences},
url = {https://www.pnas.org/content/106/Supplement_1/10061},
date = {2009},
doi = {10.1073/pnas.0904433106},
eprint = {https://www.pnas.org/content/106/Supplement_1/10061.full.pdf},
issn = {0027-8424},
journaltitle = {Proceedings of the National Academy of Sciences},
number = {Supplement 1},
pages = {10061--10065},
title = {Darwin{’}s {“}strange inversion of reasoning{”}},
volume = {106}
}
@misc{devlin2018,
title = {BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
author = {Jacob Devlin and Ming-Wei Chang and Kenton Lee and Kristina Toutanova},
year = {2018},
eprint = {1810.04805},
archiveprefix = {arXiv},
primaryclass = {cs.CL}
}
@article{doob1996,
author = {Doob, Joseph L.},
publisher = {Mathematical Association of America},
date = {1996},
issn = {00029890, 19300972},
number = {7},
pages = {586--595},
title = {The Development of Rigor in Mathematical Probability (1900-1950)},
volume = {103}
}
@inproceedings{dziugaite2017,
author = {Gintare Karolina Dziugaite and
Daniel M. Roy},
editor = {Gal Elidan and
Kristian Kersting and
Alexander T. Ihler},
title = {Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural
Networks with Many More Parameters than Training Data},
booktitle = {Proceedings of the Thirty-Third Conference on Uncertainty in Artificial
Intelligence, {UAI} 2017, Sydney, Australia, August 11-15, 2017},
publisher = {{AUAI} Press},
year = {2017},
url = {http:https://auai.org/uai2017/proceedings/papers/173.pdf},
timestamp = {Thu, 12 Mar 2020 11:3114 +0100},
biburl = {https://dblp.org/rec/conf/uai/DziugaiteR17.bib}
}
@book{farrington2016,
author = {Farrington, Karen},
title = {The blitzed city: the destruction of Coventry, 1940},
publisher = {Aurum Press},
year = {2016},
address = {London},
isbn = {978-1781313268}
}
@book{feynman1994,
author = {Feynman, Richard},
language = {english},
publisher = {Modern Library},
date = {1994},
isbn = {0-679-60127-9},
title = {The Character of Physical Law}
}
@book{gardner1959,
author = {Gardner, Martin},
publisher = {McGraw-Hill Book Company},
date = {1959},
title = {Logic machines and diagrams}
}
@article{gatys2015,
author = {Gatys, Leon A. and Ecker, Alexander S. and Bethge, Matthias},
date = {2015},
eprint = {1508.06576},
title = {A Neural Algorithm of Artistic Style},
volume = {abs/1508.06576}
}
@misc{geman1988,
title = {Neurocomputing: foundations of research, chapter Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images},
author = {Geman, Stuart and Geman, Donald},
year = {1988},
publisher = {MIT Press, Cambridge, MA, USA}
}
@misc{geron2018,
author = {Géron, Aurélien},
publisher = {Youtube},
url = {https://youtu.be/ErfnhcEV1O8},
date = {2018-02-05},
note = {[Online; Last accessed on 2020-03-08.]},
title = {A Short Introduction to Entropy, Cross-Entropy and KL-Divergence}
}
@incollection{gleiser2018,
title = {The Map and the Territory},
author = {Gleiser, Marcelo and Sowinski, Damian},
isbn = {9783319724782},
issn = {2197-6619},
doi = {10.1007/978-3-319-72478-2},
journal = {The Frontiers Collection},
year = {2018},
publisher = {Springer International Publishing},
editor = {Shyam Wuppuluri and Francisco Antonio Doria}
}
@misc{goldfeld2019,
title = {Estimating Information Flow in {DNN}s},
author = {Ziv Goldfeld and Ewout van den Berg and Kristjan Greenewald and Brian Kingsbury and Igor Melnyk and Nam Nguyen and Yury Polyanskiy},
year = {2019},
url = {https://openreview.net/forum?id=HkxOoiAcYX}
}
@book{goodfellow2016,
author = {Goodfellow, Ian J. and Bengio, Yoshua and Courville, Aaron C.},
publisher = {{MIT} Press},
date = {2016},
isbn = {9780-262-0356-1-3},
series = {Adaptive computation and machine learning},
title = {Deep Learning}
}
@inproceedings{guillaumin2012,
author = {Guillaumin, M. and Ferrari, V.},
publisher = {IEEE},
booktitle = {2012 {IEEE} Conference on Computer Vision and Pattern Recognition},
date = {2012-06},
doi = {10.1109/cvpr.2012.6248055},
title = {Large-scale knowledge transfer for object localization in {ImageNet}}
}
@report{guth2019,
author = {Guth, Fred and de-Campos, Teofilo Emidio},
institution = {UnB},
date = {2019},
eprint = {1912.08812},
eprintclass = {cs.DL},
eprinttype = {arXiv},
title = {Research Frontiers in Transfer Learning -- a systematic and bibliometric review}
}
@report{guth2019transferability,
author = {Guth, Fred},
institution = {UnB},
date = {2019-06},
title = {An Information Theoretical Transferability Metric},
type = {techreport}
}
@report{guth2020InfoDropoutReview,
author = {Guth, Fred},
institution = {UnB},
title = {Achille and Soatto, Information Dropout: Learning Optimal Representations Through Noisy Computation: A Review},
date = {2020-08-04}
}
@article{hafez-kolahi2019,
author = {Hafez-Kolahi, Hassan and Kasaei, Shohreh},
date = {2019},
title = {Information Bottleneck and its Applications in Deep Learning}
}
@article{haussler1988,
author = {Haussler, David},
publisher = {Elsevier},
date = {1988},
journaltitle = {Artificial intelligence},
number = {2},
pages = {177--221},
title = {Quantifying inductive bias: AI learning algorithms and Valiant's learning framework},
volume = {36}
}
@book{haykin2007,
title = {Neural networks: a comprehensive foundation},
author = {Haykin, Simon},
year = {2007},
publisher = {Prentice-Hall, Inc.}
}
@inproceedings{higgins2017,
title = {beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework},
author = {Irina Higgins and Lo{\"i}c Matthey and Arka Pal and Christopher Burgess and Xavier Glorot and Matthew M Botvinick and Shakir Mohamed and Alexander Lerchner},
booktitle = {ICLR},
year = {2017}
}
@inproceedings{hinton1993,
title = {Keeping the neural networks simple by minimizing the description length of the weights},
author = {Hinton, Geoffrey E and Van Camp, Drew},
booktitle = {Proceedings of the sixth annual conference on Computational learning theory},
pages = {5--13},
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