Add autoencoders section in State of the Art

assets/bibliography.bib

@@ -810,3 +810,207 @@
   edition         = {Third},
   pages           = {209, 260},
 }
+
+@article{ABIODUN2018e00938,
+  title           = {State-of-the-art in artificial neural network applications:
+                  A survey},
+  journal         = {Heliyon},
+  volume          = 4,
+  number          = 11,
+  pages           = {e00938},
+  year            = 2018,
+  issn            = {2405-8440},
+  doi             = {https://doi.org/10.1016/j.heliyon.2018.e00938},
+  url             = {https://www.sciencedirect.com/science/article/pii/S2405844018332067},
+  author          = {Oludare Isaac Abiodun and Aman Jantan and Abiodun Esther
+                  Omolara and Kemi Victoria Dada and Nachaat AbdElatif Mohamed
+                  and Humaira Arshad},
+  keywords        = {Computer science},
+  abstract        = {This is a survey of neural network applications in the
+                  real-world scenario. It provides a taxonomy of artificial
+                  neural networks (ANNs) and furnish the reader with knowledge
+                  of current and emerging trends in ANN applications research
+                  and area of focus for researchers. Additionally, the study
+                  presents ANN application challenges, contributions, compare
+                  performances and critiques methods. The study covers many
+                  applications of ANN techniques in various disciplines which
+                  include computing, science, engineering, medicine,
+                  environmental, agriculture, mining, technology, climate,
+                  business, arts, and nanotechnology, etc. The study assesses
+                  ANN contributions, compare performances and critiques methods.
+                  The study found that neural-network models such as feedforward
+                  and feedback propagation artificial neural networks are
+                  performing better in its application to human problems.
+                  Therefore, we proposed feedforward and feedback propagation
+                  ANN models for research focus based on data analysis factors
+                  like accuracy, processing speed, latency, fault tolerance,
+                  volume, scalability, convergence, and performance. Moreover,
+                  we recommend that instead of applying a single method, future
+                  research can focus on combining ANN models into one
+                  network-wide application.}
+}
+
+@article{LIU201711,
+  title           = {A survey of deep neural network architectures and their
+                  applications},
+  journal         = {Neurocomputing},
+  volume          = 234,
+  pages           = {11-26},
+  year            = 2017,
+  issn            = {0925-2312},
+  doi             = {https://doi.org/10.1016/j.neucom.2016.12.038},
+  url             = {https://www.sciencedirect.com/science/article/pii/S0925231216315533},
+  author          = {Weibo Liu and Zidong Wang and Xiaohui Liu and Nianyin Zeng
+                  and Yurong Liu and Fuad E. Alsaadi},
+  keywords        = {Autoencoder, Convolutional neural network, Deep learning,
+                  Deep belief network, Restricted Boltzmann machine},
+  abstract        = {Since the proposal of a fast learning algorithm for deep
+                  belief networks in 2006, the deep learning techniques have
+                  drawn ever-increasing research interests because of their
+                  inherent capability of overcoming the drawback of traditional
+                  algorithms dependent on hand-designed features. Deep learning
+                  approaches have also been found to be suitable for big data
+                  analysis with successful applications to computer vision,
+                  pattern recognition, speech recognition, natural language
+                  processing, and recommendation systems. In this paper, we
+                  discuss some widely-used deep learning architectures and their
+                  practical applications. An up-to-date overview is provided on
+                  four deep learning architectures, namely, autoencoder,
+                  convolutional neural network, deep belief network, and
+                  restricted Boltzmann machine. Different types of deep neural
+                  networks are surveyed and recent progresses are summarized.
+                  Applications of deep learning techniques on some selected
+                  areas (speech recognition, pattern recognition and computer
+                  vision) are highlighted. A list of future research topics are
+                  finally given with clear justifications.}
+}
+
+@misc{chervinskii_2015,
+  title           = {Autoencoder structure},
+  url             = {https://commons.wikimedia.org/wiki/File:Autoencoder_structure.png},
+  journal         = {Wikimedia},
+  author          = {Chervinskii},
+  year            = 2015,
+  month           = {Dec}
+}
+
+@book{Goodfellow-et-al-2016,
+  title           = {Deep Learning},
+  author          = {Ian Goodfellow and Yoshua Bengio and Aaron Courville},
+  publisher       = {MIT Press},
+  note            = {\url{http://www.deeplearningbook.org}},
+  year            = 2016
+}
+
+@Article{Lewis_2020,
+  author          = {Lewis, Mike and Liu, Yinhan and Goyal, Naman and
+                  Ghazvininejad, Marjan and Mohamed, Abdelrahman and Levy, Omer
+                  and Stoyanov, Veselin and Zettlemoyer, Luke},
+  title           = {BART: Denoising Sequence-to-Sequence Pre-training for
+                  Natural Language Generation, Translation, and Comprehension},
+  journal         = {Proceedings of the 58th Annual Meeting of the Association
+                  for Computational Linguistics},
+  year            = 2020,
+  doi             = {10.18653/v1/2020.acl-main.703},
+  url             = {http://dx.doi.org/10.18653/v1/2020.acl-main.703},
+  publisher       = {Association for Computational Linguistics}
+}
+
+@article{bigdeli17_image_restor_using_autoen_prior,
+  author          = {Bigdeli, Siavash Arjomand and Zwicker, Matthias},
+  title           = {Image Restoration Using Autoencoding Priors},
+  journal         = {CoRR},
+  year            = 2017,
+  url             = {http://arxiv.org/abs/1703.09964v1},
+  abstract        = {We propose to leverage denoising autoencoder networks as
+                  priors to address image restoration problems. We build on the
+                  key observation that the output of an optimal denoising
+                  autoencoder is a local mean of the true data density, and the
+                  autoencoder error (the difference between the output and input
+                  of the trained autoencoder) is a mean shift vector. We use the
+                  magnitude of this mean shift vector, that is, the distance to
+                  the local mean, as the negative log likelihood of our natural
+                  image prior. For image restoration, we maximize the likelihood
+                  using gradient descent by backpropagating the autoencoder
+                  error. A key advantage of our approach is that we do not need
+                  to train separate networks for different image restoration
+                  tasks, such as non-blind deconvolution with different kernels,
+                  or super-resolution at different magnification factors. We
+                  demonstrate state of the art results for non-blind
+                  deconvolution and super-resolution using the same autoencoding
+                  prior.},
+  archivePrefix   = {arXiv},
+  eprint          = {1703.09964},
+  primaryClass    = {cs.CV},
+}
+
+@article{makhzani15_adver_autoen,
+  author          = {Makhzani, Alireza and Shlens, Jonathon and Jaitly, Navdeep
+                  and Goodfellow, Ian and Frey, Brendan},
+  title           = {Adversarial Autoencoders},
+  journal         = {CoRR},
+  year            = 2015,
+  url             = {http://arxiv.org/abs/1511.05644v2},
+  abstract        = {In this paper, we propose the "adversarial autoencoder"
+                  (AAE), which is a probabilistic autoencoder that uses the
+                  recently proposed generative adversarial networks (GAN) to
+                  perform variational inference by matching the aggregated
+                  posterior of the hidden code vector of the autoencoder with an
+                  arbitrary prior distribution. Matching the aggregated
+                  posterior to the prior ensures that generating from any part
+                  of prior space results in meaningful samples. As a result, the
+                  decoder of the adversarial autoencoder learns a deep
+                  generative model that maps the imposed prior to the data
+                  distribution. We show how the adversarial autoencoder can be
+                  used in applications such as semi-supervised classification,
+                  disentangling style and content of images, unsupervised
+                  clustering, dimensionality reduction and data visualization.
+                  We performed experiments on MNIST, Street View House Numbers
+                  and Toronto Face datasets and show that adversarial
+                  autoencoders achieve competitive results in generative
+                  modeling and semi-supervised classification tasks.},
+  archivePrefix   = {arXiv},
+  eprint          = {1511.05644v2},
+  primaryClass    = {cs.LG},
+}
+
+@Article{Yoo_2020,
+  author          = {Yoo, Jaeyoung and Lee, Hojun and Kwak, Nojun},
+  title           = {Unpriortized Autoencoder For Image Generation},
+  journal         = {2020 IEEE International Conference on Image Processing
+                  (ICIP)},
+  year            = 2020,
+  month           = {Oct},
+  doi             = {10.1109/icip40778.2020.9191173},
+  url             = {http://dx.doi.org/10.1109/ICIP40778.2020.9191173},
+  ISBN            = 9781728163956,
+  publisher       = {IEEE}
+}
+@article{kaiser18_discr_autoen_sequen_model,
+  author          = {Kaiser, Łukasz and Bengio, Samy},
+  title           = {Discrete Autoencoders for Sequence Models},
+  journal         = {CoRR},
+  year            = 2018,
+  url             = {http://arxiv.org/abs/1801.09797v1},
+  abstract        = {Recurrent models for sequences have been recently
+                  successful at many tasks, especially for language modeling and
+                  machine translation. Nevertheless, it remains challenging to
+                  extract good representations from these models. For instance,
+                  even though language has a clear hierarchical structure going
+                  from characters through words to sentences, it is not apparent
+                  in current language models. We propose to improve the
+                  representation in sequence models by augmenting current
+                  approaches with an autoencoder that is forced to compress the
+                  sequence through an intermediate discrete latent space. In
+                  order to propagate gradients though this discrete
+                  representation we introduce an improved semantic hashing
+                  technique. We show that this technique performs well on a
+                  newly proposed quantitative efficiency measure. We also
+                  analyze latent codes produced by the model showing how they
+                  correspond to words and phrases. Finally, we present an
+                  application of the autoencoder-augmented model to generating
+                  diverse translations.},
+  archivePrefix   = {arXiv},
+  eprint          = {1801.09797v1},
+  primaryClass    = {cs.LG},
+}