@inproceedings{inproceedings,
author = {Blei, David and Ng, Andrew and Jordan, Michael},
year = {2001},
month = {01},
pages = {601-608},
title = {Latent Dirichlet Allocation},
volume = {3},
journal = {The Journal of Machine Learning Research}
}

@misc{srivastava2017autoencoding,
      title={Autoencoding Variational Inference For Topic Models}, 
      author={Akash Srivastava and Charles Sutton},
      year={2017},
      eprint={1703.01488},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@inproceedings{bianchi-etal-2021-pre,
    title = "Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence",
    author = "Bianchi, Federico  and
      Terragni, Silvia  and
      Hovy, Dirk",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-short.96",
    doi = "10.18653/v1/2021.acl-short.96",
    pages = "759--766",
    abstract = "Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data. However, the resulting word groups are often not coherent, making them harder to interpret. Recently, neural topic models have shown improvements in overall coherence. Concurrently, contextual embeddings have advanced the state of the art of neural models in general. In this paper, we combine contextualized representations with neural topic models. We find that our approach produces more meaningful and coherent topics than traditional bag-of-words topic models and recent neural models. Our results indicate that future improvements in language models will translate into better topic models.",
}

@inproceedings{conf/icra/AlexandrovaTC15,
  added-at = {2017-05-22T00:00:00.000+0200},
  author = {Alexandrova, Sonya and Tatlock, Zachary and Cakmak, Maya},
  biburl = {https://www.bibsonomy.org/bibtex/287645ab0422efa683fa79daaf4b4acde/dblp},
  booktitle = {ICRA},
  crossref = {conf/icra/2015},
  ee = {https://doi.org/10.1109/ICRA.2015.7139973},
  interhash = {e08aee21f941dced30cd6825d8bc2866},
  intrahash = {87645ab0422efa683fa79daaf4b4acde},
  isbn = {978-1-4799-6923-4},
  keywords = {dblp},
  pages = {5537-5544},
  publisher = {IEEE},
  timestamp = {2019-10-17T13:12:36.000+0200},
  title = {RoboFlow: A flow-based visual programming language for mobile manipulation tasks.},
  url = {http://dblp.uni-trier.de/db/conf/icra/icra2015.html#AlexandrovaTC15},
  year = 2015
}

@inproceedings{NIPS2015_14bfa6bb,
 author = {Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks},
 url = {https://proceedings.neurips.cc/paper/2015/file/14bfa6bb14875e45bba028a21ed38046-Paper.pdf},
 volume = {28},
 year = {2015}
}

@book{grinberg2018flask,
  title={Flask web development: developing web applications with python},
  author={Grinberg, Miguel},
  year={2018},
  publisher={" O'Reilly Media, Inc."}
}

@article{10.5555/944919.944937,
author = {Blei, David M. and Ng, Andrew Y. and Jordan, Michael I.},
title = {Latent Dirichlet Allocation},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.},
journal = {J. Mach. Learn. Res.},
month = {mar},
pages = {993–1022},
numpages = {30}
}

@article{wang2022yolov7,
  title={YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
  author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
  journal={arXiv preprint arXiv:2207.02696},
  year={2022}
}