feedback wip
This commit is contained in:
Jan Kowalczyk
218
thesis/Main.bbl
218
thesis/Main.bbl
@@ -275,6 +275,180 @@
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\endverb
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\keyw{outlier detection,Anomaly detection}
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\endentry
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given={Vladimir},
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}
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\list{publisher}{1}{%
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{Springer}%
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}
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\field{journaltitle}{Machine learning}
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\field{number}{3}
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\field{title}{Support-vector networks}
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\field{volume}{20}
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\field{year}{1995}
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\field{pages}{273\bibrangedash 297}
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\range{pages}{25}
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family={Lloyd},
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\field{journaltitle}{IEEE Transactions on Information Theory}
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\field{number}{2}
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\field{title}{Least squares quantization in PCM}
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\field{volume}{28}
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\field{year}{1982}
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\field{pages}{129\bibrangedash 137}
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\range{pages}{9}
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\verb{doi}
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\verb 10.1109/TIT.1982.1056489
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\endverb
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\keyw{Noise;Quantization (signal);Voltage;Receivers;Pulse modulation;Sufficient conditions;Stochastic processes;Probabilistic logic;Urban areas;Q measurement}
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\endentry
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{{hash=2c062e64ed26aacc08a62155e7944f04}{%
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{{hash=2dda16c0a5d50fc830d0d4a3787937fa}{%
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family={Xu},
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familyi={X\bibinitperiod},
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given={Xiaowei},
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giveni={X\bibinitperiod}}}%
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}
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{{hash=ebe3c105175ad500b489b3be8fab0279}{%
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family={Simoudis},
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familyi={S\bibinitperiod},
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given={Evangelos},
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giveni={E\bibinitperiod}}}%
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{{hash=7cacfe272c4d395c979d6aecd2f5ec9c}{%
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family={Han},
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familyi={H\bibinitperiod},
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given={Jiawei},
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giveni={J\bibinitperiod}}}%
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{{hash=d72660528ebbfc30c6661be74afda5c2}{%
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giveni={U\bibinitperiod\bibinitdelim M\bibinitperiod}}}%
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}
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\list{publisher}{1}{%
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{AAAI Press}%
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}
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\field{labeltitlesource}{title}
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\field{booktitle}{KDD}
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\field{isbn}{1-57735-004-9}
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\field{title}{A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.}
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\field{year}{1996}
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\field{pages}{226\bibrangedash 231}
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\range{pages}{6}
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\verb{urlraw}
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\verb http://dblp.uni-trier.de/db/conf/kdd/kdd96.html#EsterKSX96
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\endverb
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\verb{url}
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\verb http://dblp.uni-trier.de/db/conf/kdd/kdd96.html#EsterKSX96
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\endverb
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\endentry
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{{hash=716c197f50c5e070b09b67f32636d3e7}{%
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family={F.R.S.},
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familyi={F\bibinitperiod},
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given={Karl\bibnamedelima Pearson},
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giveni={K\bibinitperiod\bibinitdelim P\bibinitperiod}}}%
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}
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\list{publisher}{1}{%
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{Taylor & Francis}%
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}
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\field{journaltitle}{The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science}
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\field{number}{11}
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\field{title}{LIII. On lines and planes of closest fit to systems of points in space}
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\field{volume}{2}
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\field{year}{1901}
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\field{pages}{559\bibrangedash 572}
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\range{pages}{14}
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\verb{doi}
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\verb 10.1080/14786440109462720
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\endverb
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\endentry
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{{hash=002c037bd5c44a3c55a7523254ff0522}{%
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@@ -322,8 +496,8 @@
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@@ -574,8 +748,8 @@
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@@ -667,8 +841,8 @@
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\field{abstract}{Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection. Those approaches which do exist involve networks trained to perform a task other than anomaly detection, namely generative models or compression, which are in turn adapted for use in anomaly detection; they are not trained on an anomaly detection based objective. In this paper we introduce a new anomaly detection method—Deep Support Vector Data Description—, which is trained on an anomaly detection based objective. The adaptation to the deep regime necessitates that our neural network and training procedure satisfy certain properties, which we demonstrate theoretically. We show the effectiveness of our method on MNIST and CIFAR-10 image benchmark datasets as well as on the detection of adversarial examples of GTSRB stop signs.}
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@@ -720,6 +894,38 @@
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\verb https://lilianweng.github.io/posts/2018-08-12-vae/
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\endverb
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\endentry
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{{hash=9bf3bf02cd4427c0d9eab547e61fc6ff}{%
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family={Linsker},
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familyi={L\bibinitperiod},
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given={R.},
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giveni={R\bibinitperiod}}}%
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}
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\field{journaltitle}{Computer}
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\field{number}{3}
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\field{title}{Self-organization in a perceptual network}
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\field{volume}{21}
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\field{year}{1988}
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\field{pages}{105\bibrangedash 117}
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\range{pages}{13}
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\verb{doi}
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\verb 10.1109/2.36
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\endverb
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\keyw{Intelligent networks;Biological information theory;Circuits;Biology computing;Animal structures;Neuroscience;Genetics;System testing;Neural networks;Constraint theory}
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\endentry
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\entry{bg_autoencoder_ad}{inbook}{}{}
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\name{author}{4}{}{%
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{{hash=976ff3d638254bc84287783be910c8ab}{%
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