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1. Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks

   https://doi.org/10.1109/ISQED48828.2020.9136987  

   Behnia, Farnaz ; Mirzaeian, Ali ; Sabokrou, Mohammad ; Manoj, Saj ; Mohsenin, Tinoosh ; Khasawneh, Khaled N. ; Zhao, Liang ; Homayoun, Houman ; Sasan, Avesta ( March 2020 , 2020 21st International Symposium on Quality Electronic Design (ISQED))

   null (Ed.)

   In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even by decreasing the overall models' computational complexity. More specifically, we propose a stacked encoder-convolutional model, in which the input image is first encoded by the encoder module of a denoising auto-encoder, and then the resulting latent representation (without being decoded) is fed to a reduced complexity CNN for image classification. We illustrate that this network not only is more robust to adversarial examples but also has a significantly lower computational complexity when compared to the prior art defenses. 

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2. Stability Enhanced Large-Margin Classifier Selection

   https://doi.org/10.5705/ss.202016.0260  

   Sun, Will Wei ; Cheng, Guang ; Liu, Yufeng ( January 2018 , Statistica Sinica)
3. Towards a Robust and Efficient Classifier for Real World Radio Signal Modulation Classification

   https://doi.org/10.1109/ICASSP49357.2023.10094907  

   Liu, Dancheng ; Ergun, Kazim ; Rosing, Tajana Šimunić ( June 2023 , IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))
4. Intrinsically Bayesian robust classifier for single-cell gene expression trajectories in gene regulatory networks

   https://doi.org/10.1186/s12918-018-0549-y  

   Karbalayghareh, Alireza ; Braga-Neto, Ulisses ; Dougherty, Edward R. ( March 2018 , BMC Systems Biology)
5. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses

   https://doi.org/10.1186/s40168-020-00990-y  

   Guo, Jiarong ; Bolduc, Ben ; Zayed, Ahmed A. ; Varsani, Arvind ; Dominguez-Huerta, Guillermo ; Delmont, Tom O. ; Pratama, Akbar Adjie ; Gazitúa, M. Consuelo ; Vik, Dean ; Sullivan, Matthew B. ; et al ( December 2021 , Microbiome)

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   Abstract Background Viruses are a significant player in many biosphere and human ecosystems, but most signals remain “hidden” in metagenomic/metatranscriptomic sequence datasets due to the lack of universal gene markers, database representatives, and insufficiently advanced identification tools. Results Here, we introduce VirSorter2, a DNA and RNA virus identification tool that leverages genome-informed database advances across a collection of customized automatic classifiers to improve the accuracy and range of virus sequence detection. When benchmarked against genomes from both isolated and uncultivated viruses, VirSorter2 uniquely performed consistently with high accuracy (F1-score > 0.8) across viral diversity, while all other tools under-detected viruses outside of the group most represented in reference databases (i.e., those in the order Caudovirales ). Among the tools evaluated, VirSorter2 was also uniquely able to minimize errors associated with atypical cellular sequences including eukaryotic genomes and plasmids. Finally, as the virosphere exploration unravels novel viral sequences, VirSorter2’s modular design makes it inherently able to expand to new types of viruses via the design of new classifiers to maintain maximal sensitivity and specificity. Conclusion With multi-classifier and modular design, VirSorter2 demonstrates higher overall accuracy across major viral groups and will advance our knowledge of virus evolution, diversity, and virus-microbe interaction in various ecosystems. Source code of VirSorter2 is freely available ( https://bitbucket.org/MAVERICLab/virsorter2 ), and VirSorter2 is also available both on bioconda and as an iVirus app on CyVerse ( https://de.cyverse.org/de ). 

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