A survey on deep learning for cybersecurity: Progress, challenges, and opportunities

M Macas, C Wu, W Fuertes - Computer Networks, 2022 - Elsevier
As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …

Retrieving and reading: A comprehensive survey on open-domain question answering

F Zhu, W Lei, C Wang, J Zheng, S Poria… - ar** predictive models includes many stages. Most resources focus
on the modeling algorithms but neglect other critical aspects of the modeling process. This …

Epidemiological data from the COVID-19 outbreak, real-time case information

B Xu, B Gutierrez, S Mekaru, K Sewalk, L Goodwin… - Scientific data, 2020 - nature.com
Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in
December 2019 and have since spread across the world. Epidemiological studies have …

Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey

G Nguyen, S Dlugolinsky, M Bobák, V Tran… - Artificial Intelligence …, 2019 - Springer
The combined impact of new computing resources and techniques with an increasing
avalanche of large datasets, is transforming many research areas and may lead to …

Machine unlearning of features and labels

A Warnecke, L Pirch, C Wressnegger… - arxiv preprint arxiv …, 2021 - arxiv.org
Removing information from a machine learning model is a non-trivial task that requires to
partially revert the training process. This task is unavoidable when sensitive data, such as …

Hash layers for large sparse models

S Roller, S Sukhbaatar… - advances in neural …, 2021 - proceedings.neurips.cc
We investigate the training of sparse layers that use different parameters for different inputs
based on hashing in large Transformer models. Specifically, we modify the feedforward …