/
references.bib
201 lines (198 loc) · 7.64 KB
/
references.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
%TODO: use inbook for various references to goodfellow
@book{goodfellow,
title={Deep Learning},
author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
publisher={MIT Press},
note={\url{http://www.deeplearningbook.org}},
year={2016}
}
@article{universal-approximator,
author = "Moshe Leshno, Vladimir Ya. Lin, Allan Pinkus and Shimon Schocken",
title = "Multilayer Feedforward Networks With a Nonpolynomial
Activation Function Can Approximate Any Function",
journal = "Neural Networks",
?_volume = "6",
?_number = "",
?_pages = "861-867",
year = "1993",
?_month = "March",
?_note = ""
}
@article{noise-hidden-layers,
author = {Ben Poole and
Jascha Sohl{-}Dickstein and
Surya Ganguli},
title = {Analyzing noise in autoencoders and deep networks},
journal = {CoRR},
volume = {abs/1406.1831},
year = {2014},
url = {http://arxiv.org/abs/1406.1831},
archivePrefix = {arXiv},
eprint = {1406.1831},
timestamp = {Mon, 13 Aug 2018 16:48:58 +0200},
biburl = {https://dblp.org/rec/journals/corr/PooleSG14.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{batchnorm,
author = {Sergey Ioffe and
Christian Szegedy},
title = {Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift},
journal = {CoRR},
volume = {abs/1502.03167},
year = {2015},
url = {http://arxiv.org/abs/1502.03167},
archivePrefix = {arXiv},
eprint = {1502.03167},
timestamp = {Mon, 13 Aug 2018 16:47:06 +0200},
biburl = {https://dblp.org/rec/journals/corr/IoffeS15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{dropout,
author = {Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov},
title = {Dropout: A Simple Way to Prevent Neural Networks from Overfitting},
journal = {Journal of Machine Learning Research},
year = {2014},
volume = {15},
number = {56},
pages = {1929-1958},
url = {http://jmlr.org/papers/v15/srivastava14a.html}
}
@article{resnet,
author = {Kaiming He and
Xiangyu Zhang and
Shaoqing Ren and
Jian Sun},
title = {Deep Residual Learning for Image Recognition},
journal = {CoRR},
volume = {abs/1512.03385},
year = {2015},
url = {http://arxiv.org/abs/1512.03385},
archivePrefix = {arXiv},
eprint = {1512.03385},
timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
biburl = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{vae,
author = {Diederik P. Kingma and
Max Welling},
title = {An Introduction to Variational Autoencoders},
journal = {CoRR},
volume = {abs/1906.02691},
year = {2019},
url = {http://arxiv.org/abs/1906.02691},
archivePrefix = {arXiv},
eprint = {1906.02691},
timestamp = {Thu, 13 Jun 2019 13:36:00 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1906-02691.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{attention,
author = {Ashish Vaswani and
Noam Shazeer and
Niki Parmar and
Jakob Uszkoreit and
Llion Jones and
Aidan N. Gomez and
Lukasz Kaiser and
Illia Polosukhin},
title = {Attention Is All You Need},
journal = {CoRR},
volume = {abs/1706.03762},
year = {2017},
url = {http://arxiv.org/abs/1706.03762},
archivePrefix = {arXiv},
eprint = {1706.03762},
timestamp = {Mon, 13 Aug 2018 16:48:37 +0200},
biburl = {https://dblp.org/rec/journals/corr/VaswaniSPUJGKP17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{bert,
author = {Jacob Devlin and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805},
timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@misc{gpt3,
title={Language Models are Few-Shot Learners},
author={Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and Pranav Shyam and Girish Sastry and Amanda Askell and Sandhini Agarwal and Ariel Herbert-Voss and Gretchen Krueger and Tom Henighan and Rewon Child and Aditya Ramesh and Daniel M. Ziegler and Jeffrey Wu and Clemens Winter and Christopher Hesse and Mark Chen and Eric Sigler and Mateusz Litwin and Scott Gray and Benjamin Chess and Jack Clark and Christopher Berner and Sam McCandlish and Alec Radford and Ilya Sutskever and Dario Amodei},
year={2020},
eprint={2005.14165},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@InProceedings{bishop-regularization,
author = {Bishop, Christopher},
title = {Regularization and Complexity Control in Feed-forward Networks},
booktitle = {Proceedings International Conference on Artificial Neural Networks ICANN'95},
year = {1995},
month = {January},
abstract = {In this paper we consider four alternative approaches to complexity control in feed-forward networks based respectively on architecture selection, regularization, early stopping, and training with noise. We show that there are close similarities between these approaches and we argue that, for most practical applications, the technique of regularization should be the method of choice.},
publisher = {EC2 et Cie},
url = {https://www.microsoft.com/en-us/research/publication/regularization-and-complexity-control-in-feed-forward-networks/},
pages = {141-148},
volume = {1},
edition = {Proceedings International Conference on Artificial Neural Networks ICANN'95},
}
@InProceedings{magnetic-positioning,
author={W. {Storms} and J. {Shockley} and J. {Raquet}},
booktitle={2010 Ubiquitous Positioning Indoor Navigation and Location Based Service},
title={Magnetic field navigation in an indoor environment},
year={2010},
volume={},
number={},
pages={1-10},
}
@article{particle-filter1,
author={J. M. {Pak} and C. K. {Ahn} and Y. S. {Shmaliy} and M. T. {Lim}},
journal={IEEE Transactions on Industrial Informatics},
title={Improving Reliability of Particle Filter-Based Localization in Wireless Sensor Networks via Hybrid Particle/FIR Filtering},
year={2015},
volume={11},
number={5},
pages={1089-1098},
}
@article{particle-filter2,
author={P. {Yang} and W. {Wu}},
journal={IEEE Transactions on Industrial Electronics},
title={Efficient Particle Filter Localization Algorithm in Dense Passive RFID Tag Environment},
year={2014},
volume={61},
number={10},
pages={5641-5651},
}
@online{apple-google,
author = {Apple and Google},
title = {Privacy-Preserving Contact Tracing},
year = 2020,
url = {https://www.google.com/covid19/exposurenotifications/},
}
@online{immuni,
author = {Ministero della Salute},
title = {Immuni},
year = 2020,
url = {https://github.com/immuni-app/immuni-documentation},
}
@online{flutter,
author = {Google},
title = {Flutter SDK},
url = {https://flutter.dev/},
}
@online{tensorflow-lite,
author = {Google},
title = {TensorFlow Lite},
url = {https://www.tensorflow.org/lite},
}
%TODO: add tensorflow, colab and flutter references