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thesis.bib
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thesis.bib
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@article{ref:losee-1997,
title={A discipline independent definition of information},
author={Losee, Robert M},
journal={Journal of the American Society for information Science},
volume={48},
number={3},
pages={254--269},
year={1997},
publisher={Wiley Online Library}
}
@article {ref:Schneidma-2003,
author = {Schneidman, Elad and Bialek, William and Berry, Michael J.},
title = {Synergy, Redundancy, and Independence in Population Codes},
volume = {23},
number = {37},
pages = {11539--11553},
year = {2003},
doi = {10.1523/JNEUROSCI.23-37-11539.2003},
publisher = {Society for Neuroscience},
abstract = {A key issue in understanding the neural code for an ensemble of neurons is the nature and strength of correlations between neurons and how these correlations are related to the stimulus. The issue is complicated by the fact that there is not a single notion of independence or lack of correlation. We distinguish three kinds: (1) activity independence; (2) conditional independence; and (3) information independence. Each notion is related to an information measure: the information between cells, the information between cells given the stimulus, and the synergy of cells about the stimulus, respectively. We show that these measures form an interrelated framework for evaluating contributions of signal and noise correlations to the joint information conveyed about the stimulus and that at least two of the three measures must be calculated to characterize a population code. This framework is compared with others recently proposed in the literature. In addition, we distinguish questions about how information is encoded by a population of neurons from how that information can be decoded. Although information theory is natural and powerful for questions of encoding, it is not sufficient for characterizing the process of decoding. Decoding fundamentally requires an error measure that quantifies the importance of the deviations of estimated stimuli from actual stimuli. Because there is no a priori choice of error measure, questions about decoding cannot be put on the same level of generality as for encoding.},
issn = {0270-6474},
URL = {http://www.jneurosci.org/content/23/37/11539},
eprint = {http://www.jneurosci.org/content/23/37/11539.full.pdf},
journal = {Journal of Neuroscience}
}
@book{ref:adams-2008,
title={Introduction to Topology: Pure and Applied},
author={Adams, C.C. and Franzosa, R.D.},
isbn={9780131848696},
lccn={01378534},
url={https://books.google.ca/books?id=W1wnAQAAIAAJ},
year={2008},
publisher={Pearson Prentice Hall}
}
@article{ref:adolphs-2018,
title = {Local {Saddle} {Point} {Optimization}: {A} {Curvature} {Exploitation}
{Approach}},
shorttitle = {Local {Saddle} {Point} {Optimization}},
url = {http://arxiv.org/abs/1805.05751},
language = {en},
urldate = {2019-01-21},
journal = {arXiv:1805.05751 [cs, math, stat]},
author = {Adolphs, Leonard and Daneshmand, Hadi and Lucchi, Aurelien and
Hofmann, Thomas},
year = {2018},
note = {arXiv: 1805.05751},
keywords = {Computer Science - Machine Learning, Mathematics - Optimization
and Control, Statistics - Machine Learning}
}
@article{ref:amari-2017,
title = {Information Geometry Connecting Wasserstein Distance and
Kullback-Leibler Divergence via the Entropy-Relaxed
Transportation Problem},
url = {http://arxiv.org/abs/1709.10219},
journal = {{arXiv}:1709.10219 [cs, math]},
author = {Amari, Shun-ichi and Karakida, Ryo and Oizumi, Masafumi},
urldate = {2018-10-18},
date = {2017-09-28},
langid = {english},
eprinttype = {arxiv},
eprint = {1709.10219}
}
@book{ref:ambrosio-2005,
location = {Boston},
title = {Gradient flows: in metric spaces and in the space of probability
measures},
isbn = {978-3-7643-2428-5},
series = {Lectures in mathematics {ETH} Zürich},
shorttitle = {Gradient flows},
pagetotal = {333},
publisher = {Birkhäuser},
author = {Ambrosio, Luigi and Gigli, Nicola and Savaré, Giuseppe},
date = {2005},
langid = {english},
keywords = {Differential equations, Parabolic, Evolution equations, Nonlinear,
Measure theory, Metric spaces, Monotone operators}
}
@article{ref:arjovsky-2017,
year = {2017},
title = {Wasserstein {GAN}},
url = {http://arxiv.org/abs/1701.07875},
journal = {{arXiv}:1701.07875 [cs, stat]},
author = {Arjovsky, Martin and Chintala, Soumith and Bottou, Léon},
urldate = {2018-10-18},
date = {2017-01-26},
langid = {english},
eprinttype = {arxiv},
eprint = {1701.07875},
keywords = {Computer Science - Machine Learning, Statistics - Machine
Learning}
}
@article{ref:arjovsky-towards-2017,
title={Towards principled methods for training generative adversarial
networks},
author={Arjovsky, Martin and Bottou, L{\'e}on},
journal={arXiv preprint arXiv:1701.04862},
year={2017}
}
@book{ref:bishop,
author = {Bishop, Christopher M.},
title = {Pattern Recognition and Machine Learning (Information Science and
Statistics)},
year = {2006},
isbn = {0387310738},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
}
@article{ref:bojchevski-2018,
title = {{NetGAN}: Generating Graphs via Random Walks},
url = {http://arxiv.org/abs/1803.00816},
shorttitle = {{NetGAN}},
journal = {{arXiv}:1803.00816 [cs, stat]},
author = {Bojchevski, Aleksandar and Shchur, Oleksandr and Zügner, Daniel and
Günnemann, Stephan},
urldate = {2018-10-18},
date = {2018-03-02},
langid = {english},
eprinttype = {arxiv},
eprint = {1803.00816},
keywords = {Computer Science - Machine Learning, Computer Science - Social and
Information Networks, Statistics - Machine Learning}
}
@article{ref:chen-2016,
title = {{InfoGAN}: {Interpretable} {Representation} {Learning} by {Information} {Maximizing} {Generative} {Adversarial} {Nets}},
shorttitle = {{InfoGAN}},
url = {http://arxiv.org/abs/1606.03657},
language = {en},
urldate = {2019-01-21},
journal = {arXiv:1606.03657 [cs, stat]},
author = {Chen, Xi and Duan, Yan and Houthooft, Rein and Schulman, John and Sutskever, Ilya and Abbeel, Pieter},
year = {2016},
note = {arXiv: 1606.03657},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}
}
@article{ref:cheng-2018,
title={Polynomial regression as an alternative to neural nets},
author={Cheng, Xi and Khomtchouk, Bohdan and Matloff, Norman and Mohanty, Pete},
journal={arXiv preprint arXiv:1806.06850},
year={2018}
}
@misc{ref:cortes-2017,
title={Plant Disease Classification Using Convolutional Networks and Generative Adverserial Networks},
author={Cortes, Emanuel},
year=2017,
publisher={Stanford University Reports, Stanford}
}
@book{ref:cover-thomas,
title={Elements of information theory},
author={Cover, Thomas M and Thomas, Joy A},
year={2012},
publisher={John Wiley and Sons}
}
@article{ref:doan-2018,
title = {On-line Adaptative Curriculum Learning for {GANs}},
url = {http://arxiv.org/abs/1808.00020},
journal = {{arXiv}:1808.00020 [cs, stat]},
author = {Doan, Thang and Monteiro, Joao and Albuquerque, Isabela and Mazoure,
Bogdan and Durand, Audrey and Pineau, Joelle and Hjelm, R.
Devon},
urldate = {2018-10-18},
date = {2018-07-31},
langid = {english},
eprinttype = {arxiv},
eprint = {1808.00020},
keywords = {Computer Science - Machine Learning, Statistics - Machine
Learning}
}
@misc{ref:doyle,
author = {{Doyle}, Peter},
title = {Why Maximize Entropy?},
year = 1982,
url = {https://math.dartmouth.edu/~doyle/docs/whyme/whyme/whyme.html}
}
@ARTICLE{ref:endres-2003,
author={D. M. {Endres} and J. E. {Schindelin}},
journal={IEEE Transactions on Information Theory},
title={A new metric for probability distributions},
year={2003},
volume={49},
number={7},
pages={1858-1860},
keywords={probability;Bayes methods;information theory;probability distributions;bounded information-theoretically motivated metric;Bayesian interpretation;square root;asymptotic approximation;capacitory discrimination;Jensen-Shannon divergence;/spl chi//sup 2/ distance;Gaussian noise;Probability distribution;Iterative algorithms;Writing;Algorithm design and analysis;Wavelet analysis;Adaptive estimation;White noise;Bayesian methods;Convergence},
doi={10.1109/TIT.2003.813506},
ISSN={0018-9448},
month={July},}
@article{ref:flamary,
title = {Optimal transport for machine learning},
pages = {64},
author = {Flamary, Rémi},
langid = {english}
}
@inproceedings{ref:frid-2018,
title={Synthetic data augmentation using GAN for improved liver lesion classification},
author={Frid-Adar, Maayan and Klang, Eyal and Amitai, Michal and Goldberger, Jacob and Greenspan, Hayit},
booktitle={2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)},
pages={289--293},
year={2018},
organization={IEEE}
}
@article{ref:ganea-2018,
title = {Hyperbolic Neural Networks},
url = {http://arxiv.org/abs/1805.09112},
journal = {{arXiv}:1805.09112 [cs, stat]},
author = {Ganea, Octavian-Eugen and Bécigneul, Gary and Hofmann, Thomas},
urldate = {2018-10-18},
date = {2018-05-23},
langid = {english},
eprinttype = {arxiv},
eprint = {1805.09112},
keywords = {Computer Science - Machine Learning, Statistics - Machine
Learning}
}
@article{ref:gidel-negative-2018,
title = {Negative {Momentum} for {Improved} {Game} {Dynamics}},
url = {http://arxiv.org/abs/1807.04740},
language = {en},
urldate = {2019-01-21},
journal = {arXiv:1807.04740 [cs, stat]},
author = {Gidel, Gauthier and Hemmat, Reyhane Askari and Pezeshki, Mohammad
and Lepriol, Remi and Huang, Gabriel and Lacoste-Julien, Simon
and Mitliagkas, Ioannis},
year = {2018},
note = {arXiv: 1807.04740},
keywords = {Statistics - Machine Learning, Computer Science - Machine
Learning}
}
@article{ref:gidel-variational-2018,
title = {A {Variational} {Inequality} {Perspective} on {Generative}
{Adversarial} {Networks}},
url = {http://arxiv.org/abs/1802.10551},
language = {en},
urldate = {2019-01-21},
journal = {arXiv:1802.10551 [cs, math, stat]},
author = {Gidel, Gauthier and Berard, Hugo and Vignoud, Gaëtan and Vincent,
Pascal and Lacoste-Julien, Simon},
year = {2018},
note = {arXiv: 1802.10551},
keywords = {Computer Science - Machine Learning, G.1.6, I.2.6, Mathematics -
Optimization and Control, Statistics - Machine Learning}
}
@incollection{ref:goodfellow-2016,
title = {Improved Techniques for Training GANs},
author = {Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung,
Vicki and Radford, Alec and Chen, Xi and Chen, Xi},
booktitle = {Advances in Neural Information Processing Systems 29},
editor = {D. D. Lee and M. Sugiyama and U. V. Luxburg and I. Guyon and R.
Garnett},
pages = {2234--2242},
year = {2016},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/6125-improved-techniques-for-training-gans.pdf}
}
@article{ref:goodfellow-2017,
author = {Ian J. Goodfellow},
title = {{NIPS} 2016 Tutorial: Generative Adversarial Networks},
journal = {CoRR},
volume = {abs/1701.00160},
year = {2017},
url = {http://arxiv.org/abs/1701.00160},
archivePrefix = {arXiv},
eprint = {1701.00160},
timestamp = {Mon, 13 Aug 2018 16:46:12 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/Goodfellow17},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{ref:goodfellow-2019,
title = {Discriminator {Rejection} {Sampling}},
url = {http://arxiv.org/abs/1810.06758},
language = {en},
urldate = {2019-01-24},
journal = {arXiv:1810.06758 [cs, stat]},
author = {Azadi, Samaneh and Olsson, Catherine and Darrell, Trevor and
Goodfellow, Ian and Odena, Augustus},
year = {2018},
note = {arXiv: 1810.06758},
keywords = {Computer Science - Machine Learning, Statistics - Machine
Learning}
}
@book{ref:goodfellow-deep-learning-book,
title={Deep Learning},
author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
publisher={MIT Press},
note={\url{http://www.deeplearningbook.org}},
year={2016}
}
@article{ref:goodfellow-distinguishability-2014,
title = {On distinguishability criteria for estimating generative models},
url = {http://arxiv.org/abs/1412.6515},
language = {en},
urldate = {2019-01-24},
journal = {arXiv:1412.6515 [stat]},
author = {Goodfellow, Ian J.},
year = {2014},
note = {arXiv: 1412.6515},
keywords = {Statistics - Machine Learning}
}
@article{ref:goodfellow-explaining-2014,
title = {Explaining and {Harnessing} {Adversarial} {Examples}},
url = {http://arxiv.org/abs/1412.6572},
language = {en},
urldate = {2019-01-24},
journal = {arXiv:1412.6572 [cs, stat]},
author = {Goodfellow, Ian J. and Shlens, Jonathon and Szegedy, Christian},
year = {2014},
note = {arXiv: 1412.6572},
keywords = {Computer Science - Machine Learning, Statistics - Machine
Learning}
}
@article{ref:goodfellow-original,
title = {Generative {Adversarial} {Networks}},
url = {http://arxiv.org/abs/1406.2661},
language = {en},
urldate = {2019-01-24},
journal = {arXiv:1406.2661 [cs, stat]},
author = {Goodfellow, Ian J. and Pouget-Abadie, Jean and Mirza, Mehdi and Xu,
Bing and Warde-Farley, David and Ozair, Sherjil and Courville,
Aaron and Bengio, Yoshua},
year = {2014},
note = {arXiv: 1406.2661},
keywords = {Computer Science - Machine Learning, Statistics - Machine
Learning}
}
@book{ref:gray-2013,
title={Entropy and Information Theory},
author={Gray, R.M.},
isbn={9781475739824},
lccn={90211114},
url={https://books.google.ca/books?id=ZoTSBwAAQBAJ},
year={2013},
publisher={Springer New York}
}
@article{ref:gudmundsson,
title = {An Introduction to Riemannian Geometry},
pages = {130},
author = {Gudmundsson, Sigmundur},
langid = {english}
}
@article{ref:gulrajani-2017,
title = {Improved Training of Wasserstein {GANs}},
url = {http://arxiv.org/abs/1704.00028},
journal = {{arXiv}:1704.00028 [cs, stat]},
author = {Gulrajani, Ishaan and Ahmed, Faruk and Arjovsky, Martin and
Dumoulin, Vincent and Courville, Aaron},
urldate = {2018-10-18},
date = {2017-03-31},
langid = {english},
eprinttype = {arxiv},
eprint = {1704.00028},
keywords = {Computer Science - Machine Learning, Statistics - Machine
Learning}
}
@article{ref:gupta-2018,
title = {Feedback {GAN} ({FBGAN}) for {DNA}: a Novel Feedback-Loop
Architecture for Optimizing Protein Functions},
url = {http://arxiv.org/abs/1804.01694},
shorttitle = {Feedback {GAN} ({FBGAN}) for {DNA}},
journal = {{arXiv}:1804.01694 [cs, q-bio]},
author = {Gupta, Anvita and Zou, James},
urldate = {2018-10-18},
date = {2018-04-05},
langid = {english},
eprinttype = {arxiv},
eprint = {1804.01694},
keywords = {Computer Science - Machine Learning, Computer Science - Neural and
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