-
Notifications
You must be signed in to change notification settings - Fork 0
/
biblatex.bib
595 lines (538 loc) · 25.4 KB
/
biblatex.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
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
% Citations above the BibTex entries refer to the page that provided them. This file is intended
% to be used with BibLaTeX[d7e76f]. Author list marked with a * next to it means that they
% are arranged the same way they appear in the reference.
% [4ae91e, "Export Bibtex Citation" in right-side bar]
@misc{athalye2018obfuscated,
title={Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples},
author={Anish Athalye and Nicholas Carlini and David Wagner},
year={2018},
eprint={1802.00420},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
% [3b3c34, "Export Bibtex Citation" in right-side bar]
@misc{goodfellow2015explaining,
title={Explaining and Harnessing Adversarial Examples},
author={Ian J. Goodfellow and Jonathon Shlens and Christian Szegedy},
year={2015},
eprint={1412.6572},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
% [81e7d2, "Export Bibtex Citation" in right-side bar]
@misc{szegedy2014intriguing,
title={Intriguing properties of neural networks},
author={Christian Szegedy and Wojciech Zaremba and Ilya Sutskever and Joan Bruna and Dumitru Erhan and Ian Goodfellow and Rob Fergus},
year={2014},
eprint={1312.6199},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
% [82a75e, "Export Bibtex Citation" in right-side bar]
@misc{gu2015deep,
title={Towards Deep Neural Network Architectures Robust to Adversarial Examples},
author={Shixiang Gu and Luca Rigazio},
year={2015},
eprint={1412.5068},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
% [c7ca55, "Export Bibtex Citation" in right-side bar]
@misc{athalye2018synthesizing,
title={Synthesizing Robust Adversarial Examples},
author={Anish Athalye and Logan Engstrom and Andrew Ilyas and Kevin Kwok},
year={2018},
eprint={1707.07397},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
% [f856ce, "Export Bibtex Citation" in right-side bar]
@misc{madry2019deep,
title={Towards Deep Learning Models Resistant to Adversarial Attacks},
author={Aleksander Madry and Aleksandar Makelov and Ludwig Schmidt and Dimitris Tsipras and Adrian Vladu},
year={2019},
eprint={1706.06083},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
% [78168a, "Export Bibtex Citation" in right-side bar]
@misc{szegedy2015rethinking,
title={Rethinking the Inception Architecture for Computer Vision},
author={Christian Szegedy and Vincent Vanhoucke and Sergey Ioffe and Jonathon Shlens and Zbigniew Wojna},
year={2015},
eprint={1512.00567},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
% [d83ec6, "Export Bibtex Citation" in right-side bar]
@misc{kannan2018adversarial,
title={Adversarial Logit Pairing},
author={Harini Kannan and Alexey Kurakin and Ian Goodfellow},
year={2018},
eprint={1803.06373},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
% [b38463]
@article{ILSVRC15,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = {{ImageNet Large Scale Visual Recognition Challenge}},
Year = {2015},
journal = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
}
@misc{lecun,
title={The MNIST Database of Handwritten Digits},
howpublished={The MNIST Database of Handwritten Digits},
author={LeCun, Yann and Cortes, Corinna and Burges, Christopher J.C.} % *
}
@dataset{yuval,
title={Reading Digits in Natural Images with Unsupervised Feature Learning},
author={Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y.}, %*
eventtitle={{NIPS} Workshop on Deep Learning and Unsupervised Feature Learning},
url={http://ufldl.stanford.edu/housenumbers/nips2011_housenumbers.pdf},
year={2011},
note={Included bibliographic information and its best approximation to BibLaTeX entries is from \url{http://ufldl.stanford.edu/housenumbers/} (That URL's use is also requested by that site)}
}
@online{subitizingdefinition,
author={Dictionary.com},
url={https://www.dictionary.com/browse/subitize},
title={Subitize Definition \& Meaning | Dictionary.com},
urldate={2021-11-15},
}
@online{wikipediadm,
url = {https://en.wikipedia.org/wiki/Distance_matrix},
author = {various},
title = {Distance matrix - Wikipedia},
note = {Not sure which version or when it was read; likely late 2017 for the former and the same time in 2018 for the latter. Via Googling.}
}
@online{li,
title={CS231n Convolutional Neural Networks for Visual Recognition},
urldate={2021-11-10},
url={https://cs231n.github.io/optimization-2/},
author={Li, Fei-Fei and Krishna, Ranjay and Xu, Danfei},
note={I assume that authors, from ``Instructors'' of \url{http://cs231n.stanford.edu}, are listed in order of importance, so we copied that order for this reference}
}
% [8c0643, bottom]
@InProceedings{Arteta16,
author = "Arteta, C. and Lempitsky, V. and Zisserman, A.",
title = "Counting in the Wild",
booktitle = "European Conference on Computer Vision",
year = "2016",
}
% [b22ee2, "Export Bibtex Citation" in right-side bar]
@misc{simonyan2014deep,
title={Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps},
author={Karen Simonyan and Andrea Vedaldi and Andrew Zisserman},
year={2014},
eprint={1312.6034},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
% [edb77b, "bibtex" link at bottom]
@InProceedings{Zhang_2015_CVPR,
author = {Zhang, Jianming and Ma, Shugao and Sameki, Mehrnoosh and Sclaroff, Stan and Betke, Margrit and Lin, Zhe and Shen, Xiaohui and Price, Brian and Mech, Radomir},
title = {Salient Object Subitizing},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}
% What info to include into this entry and what it was was from [5bbe8a, "References"], except
% for the stuff in "notes". Andrew Zisserman was listed after Victor Lempitsky on either that site
% and/or within the paper, so I do the same here.
@inproceedings{learningtocount,
title={Learning to Count Objects in Images},
author={Lempitsky, Victor and Zisserman, Andrew},
eventtitle={Neural Information Processing Systems},
eventdate={2011},
notes={Web search with Google turned this reference up}
}
% [f8c2f4, "Export Bibtex Citation" in right-side bar]
@misc{guan2021understanding,
title={Understanding the Ability of Deep Neural Networks to Count Connected Components in Images},
author={Shuyue Guan and Murray Loew},
year={2021},
eprint={2101.01386},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
% [9b0430] (I modified the "note" field to include the semicolon and the stuff
% after it, and removed the "\" after "@MISC")
@MISC{IMM2012-03274,
author = "K. B. Petersen and M. S. Pedersen",
title = "The Matrix Cookbook",
year = "2012",
month = "nov",
keywords = "Matrix identity, matrix relations, inverse, matrix derivative",
publisher = "Technical University of Denmark",
address = "",
note = "Version 20121115; \url{https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf} was where it was originally gotten from",
url = "http://www2.compute.dtu.dk/pubdb/pubs/3274-full.html",
abstract = "Matrix identities, relations and approximations. A desktop reference for quick overview of mathematics of matrices."
}
% [9c1718, "Export Bibtex Citation" in right-side bar]
@misc{tramèr2020ensemble,
title={Ensemble Adversarial Training: Attacks and Defenses},
author={Florian Tramèr and Alexey Kurakin and Nicolas Papernot and Ian Goodfellow and Dan Boneh and Patrick McDaniel},
year={2020},
eprint={1705.07204},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
% [ef0c98]
@inproceedings{tokui2019chainer,
title={Chainer: A Deep Learning Framework for Accelerating the Research Cycle},
author={Tokui, Seiya and Okuta, Ryosuke and Akiba, Takuya and Niitani, Yusuke and Ogawa, Toru and Saito, Shunta and Suzuki, Shuji and Uenishi, Kota and Vogel, Brian and Yamazaki Vincent, Hiroyuki},
booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={2002--2011},
year={2019},
organization={ACM}
}
% [579b95]
@incollection{NEURIPS2019_9015,
title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {8024--8035},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
}
% [bf1815, "Export Bibtex Citation" in right-side bar]
@misc{yosinski2015understanding,
title={Understanding Neural Networks Through Deep Visualization},
author={Jason Yosinski and Jeff Clune and Anh Nguyen and Thomas Fuchs and Hod Lipson},
year={2015},
eprint={1506.06579},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
% [931142, "Export Bibtex Citation" in right-side bar]
@misc{ioffe2015batch,
title={Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift},
author={Sergey Ioffe and Christian Szegedy},
year={2015},
eprint={1502.03167},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
% Copied from [36027e, "Export Bibtex Citation" in right-side bar], but with an added "\\" in place of the space between
% "in" and "Batch"
@misc{ioffe2017batch,
title={Batch Renormalization: Towards Reducing Minibatch Dependence in\\Batch-Normalized Models},
author={Sergey Ioffe},
year={2017},
eprint={1702.03275},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
% [551f5f, "Export Bibtex Citation" in right-side bar]
@misc{goodfellow2013maxout,
title={Maxout Networks},
author={Ian J. Goodfellow and David Warde-Farley and Mehdi Mirza and Aaron Courville and Yoshua Bengio},
year={2013},
eprint={1302.4389},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
@misc{annotators,
title={Amazon Mechanical Turk},
author={{Amazon Mechanical Turk}},
url={https://www.mturk.com}
}
% Included the information in the "doi" and "pages" fields because it appears that
% [d92f64, "About this article"]/[17a481] wants you to do so and to use the values
% that they list there. All other fields, except for "date" (although they do specify the year, so I
% figured the date was an upgrade) were confirmed to be necessary by
% [d92f64, "About this article"] and [17a481]. Author importance (which authors
% are displayed first) is also from those references.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% $
@article{rumelhart,
title={Learning Representations by Back-Propagating Errors},
author={Rumelhart, David E. and Hinton, Geoffrey E. and Williams, Ronald J.},
date={1986-10-09},
journaltitle={Nature},
number={323},
doi={10.1038/323533a0},
pages={533-536}
}
% Paper is via [771484, "References"]
@article{lbfgs,
title={On the limited memory BFGS method for large scale optimization},
author={Liu, Dong C. and Nocedal, Jorge},
date={1989-08},
journaltitle={Mathematical Programming},
number={45},
doi={10.1007/BF01589116},
pages={503-528}
}
% %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% [15eca3, Quote button]. Had to remove the abstract field because it was causing issues
@inproceedings{10.1145/3052973.3053009,
author = {Papernot, Nicolas and McDaniel, Patrick and Goodfellow, Ian and Jha, Somesh and Celik, Z. Berkay and Swami, Ananthram},
title = {Practical Black-Box Attacks against Machine Learning},
year = {2017},
isbn = {9781450349444},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3052973.3053009},
doi = {10.1145/3052973.3053009},
booktitle = {Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security},
pages = {506–519},
numpages = {14},
keywords = {black-box attack, adversarial machine learning, machine learning},
location = {Abu Dhabi, United Arab Emirates},
series = {ASIA CCS '17}
}
% [1fea50]
@ARTICLE{2020NumPy-Array,
author = {Harris, Charles R. and Millman, K. Jarrod and
van der Walt, Stéfan J and Gommers, Ralf and
Virtanen, Pauli and Cournapeau, David and
Wieser, Eric and Taylor, Julian and Berg, Sebastian and
Smith, Nathaniel J. and Kern, Robert and Picus, Matti and
Hoyer, Stephan and van Kerkwijk, Marten H. and
Brett, Matthew and Haldane, Allan and
Fernández del Río, Jaime and Wiebe, Mark and
Peterson, Pearu and Gérard-Marchant, Pierre and
Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and
Abbasi, Hameer and Gohlke, Christoph and
Oliphant, Travis E.},
title = {Array programming with {NumPy}},
journal = {Nature},
year = {2020},
volume = {585},
pages = {357–362},
doi = {10.1038/s41586-020-2649-2}
}
% ^[af489d]
@inproceedings{cupy_learningsys2017,
author = "Okuta, Ryosuke and Unno, Yuya and Nishino, Daisuke and Hido, Shohei and Loomis, Crissman",
title = "CuPy: A NumPy-Compatible Library for NVIDIA GPU Calculations",
booktitle = "Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS)",
year = "2017",
url = "http://learningsys.org/nips17/assets/papers/paper_16.pdf"
}
@software{language,
title = {Python},
author = {{Python Software Foundation}},
url = {python.org},
version = {3}
}
% [18cb5b]
@Misc{shapely,
author = {Sean Gillies and others},
organization = {toblerity.org},
title = {Shapely: manipulation and analysis of geometric objects},
year = {2007--},
url = "https://github.com/Toblerity/Shapely"
}
@misc{imagesinpython,
title = {Pillow},
author = {Clark, Alex and {more}}, % *
url = {https://github.com/python-pillow/Pillow}
}
% [d125a1, "Export Bibtex Citation" in right-side bar]
@misc{tramèr2017space,
title={The Space of Transferable Adversarial Examples},
author={Florian Tramèr and Nicolas Papernot and Ian Goodfellow and Dan Boneh and Patrick McDaniel},
year={2017},
eprint={1704.03453},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
% [b33a7b, "Export Bibtex Citation" in right-side bar]
@misc{redmon2016look,
title={You Only Look Once: Unified, Real-Time Object Detection},
author={Joseph Redmon and Santosh Divvala and Ross Girshick and Ali Farhadi},
year={2016},
eprint={1506.02640},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
% [e76013, "Cite This" button] (removed the trailing comma, though)
@INPROCEEDINGS{937505,
author={Boykov, Y.Y. and Jolly, M.-P.},
booktitle={Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001},
title={Interactive graph cuts for optimal boundary amp; region segmentation of objects in N-D images},
year={2001},
volume={1},
number={},
pages={105-112 vol.1},
doi={10.1109/ICCV.2001.937505}}
@online{viaforshapely,
title = {algorithm - Finding if two polygons Intersect in Python? - Geographic Information Systems Stack Exchange},
author = {{radouxju} and {Martin Thoma} and {Devdatta Tengshe}},
date = {2018-09-14},
url = {https://gis.stackexchange.com/questions/90055/finding-if-two-polygons-intersect-in-python}
}
% This entry is from [959686, "Cite this Paper"] with the exception of the "note" attribute and
% the comma that follows it, which I wrote myself.
@InProceedings{pmlr-v15-glorot11a,
title = {Deep Sparse Rectifier Neural Networks},
author = {Glorot, Xavier and Bordes, Antoine and Bengio, Yoshua},
booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
pages = {315--323},
year = {2011},
editor = {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav},
volume = {15},
series = {Proceedings of Machine Learning Research},
address = {Fort Lauderdale, FL, USA},
month = {11--13 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf},
url = {https://proceedings.mlr.press/v15/glorot11a.html},
note = {Wikipedia (\url{https://en.wikipedia.org/wiki/Rectifier_(neural_networks)}) claims this reference as the one for ReLU},
abstract = {While logistic sigmoid neurons are more biologically plausible than hyperbolic tangent neurons, the latter work better for training multi-layer neural networks. This paper shows that rectifying neurons are an even better model of biological neurons and yield equal or better performance than hyperbolic tangent networks in spite of the hard non-linearity and non-differentiability at zero, creating sparse representations with true zeros which seem remarkably suitable for naturally sparse data. Even though they can take advantage of semi-supervised setups with extra-unlabeled data, deep rectifier networks can reach their best performance without requiring any unsupervised pre-training on purely supervised tasks with large labeled datasets. Hence, these results can be seen as a new milestone in the attempts at understanding the difficulty in training deep but purely supervised neural networks, and closing the performance gap between neural networks learnt with and without unsupervised pre-training. [pdf]}
}
% [7ffa04, bottom] asked to include the information in "title", "author", and "date" (and I used
% those fields as a result)
@techreport{cifar,
title = {Learning Multiple Layers of Features from Tiny Images},
author = {Krizhevsky, Alex},
institution = {University of Toronto},
url = {https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf},
date = {2009}
}
% [ab19c4]
@inproceedings{NIPS2000_44968aec,
author = {Dugas, Charles and Bengio, Yoshua and B\'{e}lisle, Fran\c{c}ois and Nadeau, Claude and Garcia, Ren\'{e}},
booktitle = {Advances in Neural Information Processing Systems},
editor = {T. Leen and T. Dietterich and V. Tresp},
pages = {},
publisher = {MIT Press},
title = {Incorporating Second-Order Functional Knowledge for Better Option Pricing},
url = {https://proceedings.neurips.cc/paper/2000/file/44968aece94f667e4095002d140b5896-Paper.pdf},
volume = {13},
year = {2001}
}
% [bf3ab8]
@article{HORNIK1989359,
title = {Multilayer feedforward networks are universal approximators},
journal = {Neural Networks},
volume = {2},
number = {5},
pages = {359-366},
year = {1989},
issn = {0893-6080},
doi = {https://doi.org/10.1016/0893-6080(89)90020-8},
url = {https://www.sciencedirect.com/science/article/pii/0893608089900208},
author = {Kurt Hornik and Maxwell Stinchcombe and Halbert White},
keywords = {Feedforward networks, Universal approximation, Mapping networks, Network representation capability, Stone-Weierstrass Theorem, Squashing functions, Sigma-Pi networks, Back-propagation networks},
abstract = {This paper rigorously establishes that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available. In this sense, multilayer feedforward networks are a class of universal approximators.}
}
@software{msftprogram,
title = {Paint 3D},
author = {{Microsoft Corporation}},
url = {https://www.microsoft.com/en-us/p/paint-3d/9nblggh5fv99},
note = {Comes with Windows 10}
}
% [03f17c, "Export Bibtex Citation" in right-side bar]
@misc{eykholt2018robust,
title={Robust Physical-World Attacks on Deep Learning Models},
author={Kevin Eykholt and Ivan Evtimov and Earlence Fernandes and Bo Li and Amir Rahmati and Chaowei Xiao and Atul Prakash and Tadayoshi Kohno and Dawn Song},
year={2018},
eprint={1707.08945},
archivePrefix={arXiv},
primaryClass={cs.CR}
}
% [617edf, "Export Bibtex Citation" in right-side bar]
@misc{szegedy2014going,
title={Going Deeper with Convolutions},
author={Christian Szegedy and Wei Liu and Yangqing Jia and Pierre Sermanet and Scott Reed and Dragomir Anguelov and Dumitru Erhan and Vincent Vanhoucke and Andrew Rabinovich},
year={2014},
eprint={1409.4842},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
% [90f6cc, "Cite paper"]
@InProceedings{10.1007/978-3-319-46487-9_48,
author="Mundhenk, T. Nathan
and Konjevod, Goran
and Sakla, Wesam A.
and Boakye, Kofi",
editor="Leibe, Bastian
and Matas, Jiri
and Sebe, Nicu
and Welling, Max",
title="A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning",
booktitle="Computer Vision -- ECCV 2016",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="785--800",
abstract="We have created a large diverse set of cars from overhead images (Data sets, annotations, networks and scripts are available from http://gdo-datasci.ucllnl.org/cowc/), which are useful for training a deep learner to binary classify, detect and count them. The dataset and all related material will be made publically available. The set contains contextual matter to aid in identification of difficult targets. We demonstrate classification and detection on this dataset using a neural network we call ResCeption. This network combines residual learning with Inception-style layers and is used to count cars in one look. This is a new way to count objects rather than by localization or density estimation. It is fairly accurate, fast and easy to implement. Additionally, the counting method is not car or scene specific. It would be easy to train this method to count other kinds of objects and counting over new scenes requires no extra set up or assumptions about object locations.",
isbn="978-3-319-46487-9"
}
@online{reeves,
title = {Teaching a Robot Dog to Pee Beer},
author = {Reeves, Michael},
url = {https://youtu.be/tqsy9Wtr1qE},
date = {2021-04-09},
note = {As a warning, given the formality of this thesis, one should be aware of the crudeness
of this video}
}
@online{mycode,
title = {{CountingPlusFriendly}},
author = {Cutilli, Benjamin},
url = {https://github.com/benvcutilli/CountingPlusFriendly}
}
@online{subitizingyoutube,
title = {Counting, Explained},
author = {{Nerdwriter1}},
url = {https://www.youtube.com/watch?v=qRMP6rCT_bs},
date = {2014-07-28}
}
% [015d90, clicked "Cite this item"]
@article{10.2307/1418556,
ISSN = {00029556},
URL = {http://www.jstor.org/stable/1418556},
author = {E. L. Kaufman and M. W. Lord and T. W. Reese and J. Volkmann},
journal = {The American Journal of Psychology},
number = {4},
pages = {498--525},
publisher = {University of Illinois Press},
title = {The Discrimination of Visual Number},
volume = {62},
year = {1949}
}
% [a7a363, "Cite" link]
@article{edselc.2-52.0-001709992019760101,
Author = {Atkinson, J. and Campbell, F.W. and Francis, M.R.},
ISSN = {03010066},
Journal = {Perception},
Number = {3},
Pages = {327-334},
Title = {The magic number 4±0: a new look at visual numerosity judgments.},
Volume = {5},
URL = {https://proxyiub.uits.iu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edselc&AN=edselc.2-52.0-0017099920&site=eds-live&scope=site},
Year = {1976},
}
% [00916e, click "Cite"]
@article{edssch.oai:escholarship.org/ark:/13030/qt9fn2777219820101,
Author = {Mandler, George and Shebo, Billie Jo},
Title = {Subitizing: An analysis of its component processes. Journal of Experimental Psychology: General, 111, 1‑22.},
URL = {https://proxyiub.uits.iu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edssch&AN=edssch.oai%3aescholarship.org%2fark%3a%2f13030%2fqt9fn27772&site=eds-live&scope=site},
Year = {1982},
}
% [1946c9]
@Article{Hunter:2007,
Author = {Hunter, J. D.},
Title = {Matplotlib: A 2D graphics environment},
Journal = {Computing in Science \& Engineering},
Volume = {9},
Number = {3},
Pages = {90--95},
abstract = {Matplotlib is a 2D graphics package used for Python for
application development, interactive scripting, and publication-quality
image generation across user interfaces and operating systems.},
publisher = {IEEE COMPUTER SOC},
doi = {10.1109/MCSE.2007.55},
year = 2007
}