@inproceedings{park2018multimodal,
  title={Multimodal explanations: Justifying decisions and pointing to the evidence},
  author={Park, Dong Huk and Hendricks, Lisa Anne and Akata, Zeynep and Rohrbach, Anna and Schiele, Bernt and Darrell, Trevor and Rohrbach, Marcus},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={8779--8788},
  year={2018}
}

@inproceedings{hendricks2018women,
  title={Women also snowboard: Overcoming bias in captioning models},
  author={Hendricks, Lisa Anne and Burns, Kaylee and Saenko, Kate and Darrell, Trevor and Rohrbach, Anna},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={771--787},
  year={2018}
}

@inproceedings{wang2020mitigating,
  title={Mitigating bias in face recognition using skewness-aware reinforcement learning},
  author={Wang, Mei and Deng, Weihong},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={9322--9331},
  year={2020}
}

@inproceedings{kortylewski2018empirically,
  title={Empirically analyzing the effect of dataset biases on deep face recognition systems},
  author={Kortylewski, Adam and Egger, Bernhard and Schneider, Andreas and Gerig, Thomas and Morel-Forster, Andreas and Vetter, Thomas},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
  pages={2093--2102},
  year={2018}
}

@inproceedings{buolamwini2018gender,
  title={Gender shades: Intersectional accuracy disparities in commercial gender classification},
  author={Buolamwini, Joy and Gebru, Timnit},
  booktitle={Conference on fairness, accountability and transparency},
  pages={77--91},
  year={2018},
  organization={PMLR}
}

@inproceedings{das2018mitigating,
  title={Mitigating bias in gender, age and ethnicity classification: a multi-task convolution neural network approach},
  author={Das, Abhijit and Dantcheva, Antitza and Bremond, Francois},
  booktitle={Proceedings of the european conference on computer vision (eccv) workshops},
  year={2018}
}

@inproceedings{yang2022explaining, % LaViSE paper
  author    = {Yang, Yu and Kim, Seungbae and Joo, Jungseock},
  title     = {Explaining Deep Convolutional Neural Networks via Unsupervised Visual-Semantic Filter Attention},
  booktitle = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2022},
  pages     = {8323-8333},
  doi       = {10.1109/CVPR52688.2022.00815}
}

@misc{lavise-repo, % GitHub Repo of LaViSE
  author = {Yang, Yu and Kim, Seungbae and Joo, Jungseock},
  title = {LaViSE},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/YuYang0901/LaViSE}},
  commit = {c16a9d179eb63086ac9f0e012f1530b4365554a2}
}

@InProceedings{coco, % COCO
  author="Lin, Tsung-Yi
  and Maire, Michael
  and Belongie, Serge
  and Hays, James
  and Perona, Pietro
  and Ramanan, Deva
  and Doll{\'a}r, Piotr
  and Zitnick, C. Lawrence",
  editor="Fleet, David
  and Pajdla, Tomas
  and Schiele, Bernt
  and Tuytelaars, Tinne",
  title="Microsoft COCO: Common Objects in Context",
  booktitle="Computer Vision -- ECCV 2014",
  year="2014",
  publisher="Springer International Publishing",
  address="Cham",
  pages="740--755",
  abstract="We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.",
  isbn="978-3-319-10602-1"
}

@inproceedings{pennington2014glove, % GloVe
  added-at = {2016-02-18T12:02:38.000+0100},
  author = {Pennington, Jeffrey and Socher, Richard and Manning, Christopher D},
  biburl = {https://www.bibsonomy.org/bibtex/2a6e77a38c13e374ab250e13ae22993ec/thoni},
  booktitle = {EMNLP},
  interhash = {29813227df1eea94efa14c7df2b5553a},
  intrahash = {a6e77a38c13e374ab250e13ae22993ec},
  keywords = {deeplearning deepwiki glove semantic},
  pages = {1532--1543},
  timestamp = {2016-09-06T08:23:07.000+0200},
  title = {Glove: Global Vectors for Word Representation.},
  volume = 14,
  year = 2014
}

@inproceedings{deng2009imagenet, % ImageNet
  title={Imagenet: A large-scale hierarchical image database},
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  booktitle={2009 IEEE conference on computer vision and pattern recognition},
  pages={248--255},
  year={2009},
  organization={Ieee}
}

@inproceedings{he2016deep, % ResNet
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

@article{paszke2019pytorch, % PyTorch
  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 others},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@article{defazio2023learning, % DAdaptAdam
  title={Learning-Rate-Free Learning by D-Adaptation},
  author={Defazio, Aaron and Mishchenko, Konstantin},
  journal={arXiv preprint arXiv:2301.07733},
  year={2023}
}

@article{krishna2017visual, % Visual Genome
  title={Visual genome: Connecting language and vision using crowdsourced dense image annotations},
  author={Krishna, Ranjay and Zhu, Yuke and Groth, Oliver and Johnson, Justin and Hata, Kenji and Kravitz, Joshua and Chen, Stephanie and Kalantidis, Yannis and Li, Li-Jia and Shamma, David A and others},
  journal={International journal of computer vision},
  volume={123},
  pages={32--73},
  year={2017},
  publisher={Springer}
}

@article{krizhevsky2017imagenet, % AlexNet
  title={Imagenet classification with deep convolutional neural networks},
  author={Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
  journal={Communications of the ACM},
  volume={60},
  number={6},
  pages={84--90},
  year={2017},
  publisher={AcM New York, NY, USA}
}

@book{Fellbaum1998,
  added-at = {2017-11-01T11:46:20.000+0100},
  address = {Cambridge, MA},
  biburl = {https://www.bibsonomy.org/bibtex/28472b4f9d7f2bfc4a97ffd4a023facc6/flint63},
  editor = {Fellbaum, Christiane},
  file = {eBook:1900-99/Fellbaum1998.pdf:PDF;MIT Press Product Page:http\://mitpress.mit.edu/books/wordnet:URL;Amazon Search inside:http\://www.amazon.de/gp/reader/026206197X/:URL},
  groups = {public},
  interhash = {42daa1681607dd1d3f3234c605d84ec3},
  intrahash = {8472b4f9d7f2bfc4a97ffd4a023facc6},
  isbn = {978-0-262-06197-1},
  keywords = {01821 101 mitpress book shelf ai language processing ontology lexicon},
  publisher = {MIT Press},
  series = {Language, Speech, and Communication},
  timestamp = {2018-04-16T11:51:58.000+0200},
  title = {WordNet: An Electronic Lexical Database},
  username = {flint63},
  year = 1998
}

@article{alom2018history,
  title={The history began from alexnet: A comprehensive survey on deep learning approaches},
  author={Alom, Md Zahangir and Taha, Tarek M and Yakopcic, Christopher and Westberg, Stefan and Sidike, Paheding and Nasrin, Mst Shamima and Van Esesn, Brian C and Awwal, Abdul A S and Asari, Vijayan K},
  journal={arXiv preprint arXiv:1803.01164},
  year={2018}
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% TEMPLATE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

@article{Rougier:2018,
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@article{Science:2018,
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@Misc{Roboto:2011,
  author =       {Christian Robertson},
  title =        {The Roboto family of fonts (Google)},
  url =          {https://github.com/google/roboto},
  year =         2011,
  note =         {Apache License, verison 2.0},
}

@Misc{SourceSerifPro:2014,
  author =       {Frank Grießhammer},
  title =        {Source Serif Pro (Adobe Systems)},
  url =          {https://github.com/adobe-fonts/source-serif-pro},
  year =         2014,
  note =         {SIL Open Font License, version 1.1},
}

@Misc{SourceCodePro:2012,
  author =       {Paul D. Hunt},
  title =        {Source Code Pro (Adobe Systems)},
  url =          {https://github.com/adobe-fonts/source-code-pro},
  year =         2012,
  note =         {SIL Open Font License, version 1.1},
}

@article{Topalidou:2015,
  author =       {Topalidou, Meropi and Rougier, Nicolas P.},
  title =        {{[Re] Interaction between cognitive and motor cortico-basal
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  journal =      {ReScience},
  year =         2015,
  volume =       1,
  number =       1,
  doi =          {10.5281/zenodo.27944},
}

@article{Rougier:2017,
  doi =          {10.7717/peerj-cs.142},
  author =       {Nicolas P. Rougier and Konrad Hinsen and Frédéric Alexandre
                  and Thomas Arildsen and Lorena Barba and Fabien
                  C. Y. Benureau and C. Titus Brown and Pierre de Buyl and Ozan
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                  G. Hall and Rafael Neto Henriques and Xavier Hinaut and Kamil
                  S Jaron and Mehdi Khamassi and Almar Klein and Tiina Manninen
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  journal =      {{PeerJ} Computer Science},
  month =        12,
  volume =       3,
  pages =        {e142},
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  github =       {https://github.com/ReScience/ReScience-article-2},
  keywords =     {journal},
  tags =         {OS},
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@article{Sinha:2022,
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  journal = {ReScience C},
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  code_swh = {},
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  review_url = {},
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@article{Sinha:2021,
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  domain = {},
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}

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  type = {Editorial},
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}

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}
