BibBase

Keyword: ⛔ no doi found

2018 (4)

Parallel WaveNet: Fast High-Fidelity Speech Synthesis. van den Oord, A., Li, Y., Babuschkin, I., Simonyan, K., Vinyals, O., Kavukcuoglu, K., van den Driessche, G., Lockhart, E., Cobo, L. C., Stimberg, F., Casagrande, N., Grewe, D., Noury, S., Dieleman, S., Elsen, E., Kalchbrenner, N., Zen, H., Graves, A., King, H., Walters, T., Belov, D., & Hassabis, D. In Dy, J. G. & Krause, A., editors, ICML, volume 80, of Proceedings of Machine Learning Research, pages 3915–3923, 2018. PMLR.

Parallel WaveNet: Fast High-Fidelity Speech Synthesis [link]

Paper bibtex

@inproceedings{van_den_oord_parallel_2018,
	series = {Proceedings of {Machine} {Learning} {Research}},
	title = {Parallel {WaveNet}: {Fast} {High}-{Fidelity} {Speech} {Synthesis}},
	volume = {80},
	url = {http://dblp.uni-trier.de/db/conf/icml/icml2018.html#OordLBSVKDLCSCG18},
	booktitle = {{ICML}},
	publisher = {PMLR},
	author = {van den Oord, Aäron and Li, Yazhe and Babuschkin, Igor and Simonyan, Karen and Vinyals, Oriol and Kavukcuoglu, Koray and van den Driessche, George and Lockhart, Edward and Cobo, Luis C. and Stimberg, Florian and Casagrande, Norman and Grewe, Dominik and Noury, Seb and Dieleman, Sander and Elsen, Erich and Kalchbrenner, Nal and Zen, Heiga and Graves, Alex and King, Helen and Walters, Tom and Belov, Dan and Hassabis, Demis},
	editor = {Dy, Jennifer G. and Krause, Andreas},
	year = {2018},
	keywords = {\#nosource, ⛔ No DOI found},
	pages = {3915--3923},
}

Linear Spectral Estimators and an Application to Phase Retrieval. Ghods, R., Lan, A. S., Goldstein, T., & Studer, C. arXiv:1806.03547, June, 2018. arXiv: http://arxiv.org/abs/1806.03547
bibtex

@article{ghods_linear_2018,
	title = {Linear {Spectral} {Estimators} and an {Application} to {Phase} {Retrieval}},
	journal = {arXiv:1806.03547},
	author = {Ghods, Ramina and Lan, Andrew S. and Goldstein, Tom and Studer, Christoph},
	month = jun,
	year = {2018},
	note = {arXiv: http://arxiv.org/abs/1806.03547},
	keywords = {/unread, cs.IT, eess.SP, math.IT, stat.ML, ⛔ No DOI found},
}

Polynomial Convergence of Gradient Descent for Training One-Hidden-Layer Neural Networks. Vempala, S. & Wilmes, J. arXiv:1805.02677, May, 2018. arXiv: 1805.02677
bibtex

@article{vempala_polynomial_2018,
	title = {Polynomial {Convergence} of {Gradient} {Descent} for {Training} {One}-{Hidden}-{Layer} {Neural} {Networks}},
	journal = {arXiv:1805.02677},
	author = {Vempala, Santosh and Wilmes, John},
	month = may,
	year = {2018},
	note = {arXiv: 1805.02677},
	keywords = {/unread, cs.LG, stat.ML, ⛔ No DOI found},
}

Rhinosinusites aiguës de l'enfant. Simon, F. Pédiatrie Pratique, 295:2–3, 2018.
bibtex

@article{simon_rhinosinusites_2018,
	title = {Rhinosinusites aiguës de l'enfant},
	volume = {295},
	copyright = {All rights reserved},
	shorttitle = {Rhinosinusites aiguës de l'enfant},
	language = {fr},
	journal = {Pédiatrie Pratique},
	author = {Simon, F.},
	year = {2018},
	keywords = {⛔ No DOI found, 🔍No DOI found},
	pages = {2--3}
}

2017 (7)

Malformation veineuse de la tête et du cou, Intérêt du laser diode. Sauvaget, E., Simon, F., Salvan, D., Faucon, B., Borsik, M., Le Clerc, N., & Bisdorff, A. Sang Thrombose Vaisseaux, 29(2):69–74, 2017.
bibtex

@article{sauvaget_malformation_2017,
	title = {Malformation veineuse de la tête et du cou, {Intérêt} du laser diode},
	volume = {29},
	copyright = {All rights reserved},
	shorttitle = {Malformation veineuse de la tête et du cou, {Intérêt} du laser diode},
	language = {fr},
	number = {2},
	journal = {Sang Thrombose Vaisseaux},
	author = {Sauvaget, E. and Simon, F. and Salvan, D. and Faucon, B. and Borsik, M. and Le Clerc, N. and Bisdorff, A.},
	year = {2017},
	keywords = {⛔ No DOI found, 🔍No DOI found},
	pages = {69--74}
}

Structured inference networks for nonlinear state space models. Krishnan, R. G., Shalit, U., & Sontag, D. In Proceedings of the thirty-first AAAI conference on artificial intelligence, of AAAI'17, pages 2101–2109, 2017. AAAI Press. Place: San Francisco, California, USA Number of pages: 9
abstract bibtex

@inproceedings{10.5555/3298483.3298543,
	series = {{AAAI}'17},
	title = {Structured inference networks for nonlinear state space models},
	abstract = {Gaussian state space models have been used for decades as generative models of sequential data. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption. We introduce a unified algorithm to efficiently learn a broad class of linear and non-linear state space models, including variants where the emission and transition distributions are modeled by deep neural networks. Our learning algorithm simultaneously learns a compiled inference network and the generative model, leveraging a structured variational approximation parameterized by recurrent neural networks to mimic the posterior distribution. We apply the learning algorithm to both synthetic and real-world datasets, demonstrating its scalability and versatility. We find that using the structured approximation to the posterior results in models with significantly higher held-out likelihood.},
	booktitle = {Proceedings of the thirty-first {AAAI} conference on artificial intelligence},
	publisher = {AAAI Press},
	author = {Krishnan, Rahul G. and Shalit, Uri and Sontag, David},
	year = {2017},
	note = {Place: San Francisco, California, USA
Number of pages: 9},
	keywords = {\#nosource, ⛔ No DOI found},
	pages = {2101--2109},
}

Robust Physical-World Attacks on Deep Learning Models. Evtimov, I., Eykholt, K., Fernandes, E., Kohno, T., Li, B., Prakash, A., Rahmati, A., & Song, D. arXiv:1707.08945 [cs], July, 2017. arXiv: 1707.08945