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Context Autoencoder for Self-supervised Representation Learning
International Journal of Computer Vision, 2022We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised representation pretraining. We pretrain an encoder by making predictions in the encoded representation space. The pretraining tasks include two tasks:
Xiaokang Chen +9 more
semanticscholar +1 more source
Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the technology of wireless and mobile communication. The data of target regions are collected by widely distributed sensing devices and transmitted to the processing center
Chunyong Yin +3 more
semanticscholar +1 more source
Neural Networks, 2023
MicroRNAs (miRNA) play critical roles in diverse biological processes of diseases. Inferring potential disease-miRNA associations enable us to better understand the development and diagnosis of complex human diseases via computational algorithms.
Yanbu Guo +3 more
semanticscholar +1 more source
MicroRNAs (miRNA) play critical roles in diverse biological processes of diseases. Inferring potential disease-miRNA associations enable us to better understand the development and diagnosis of complex human diseases via computational algorithms.
Yanbu Guo +3 more
semanticscholar +1 more source
2020
The study of psychiatric and neurologic disorders typically involves the acquisition of a wide range of different types of data, such as brain images, electronic health records, and mobile phone sensors data. Each type of data has its unique temporal and spatial characteristics, and the process of extracting useful information from them can be very ...
Lopez Pinaya, Walter Hugo +3 more
+5 more sources
The study of psychiatric and neurologic disorders typically involves the acquisition of a wide range of different types of data, such as brain images, electronic health records, and mobile phone sensors data. Each type of data has its unique temporal and spatial characteristics, and the process of extracting useful information from them can be very ...
Lopez Pinaya, Walter Hugo +3 more
+5 more sources
Deep Autoencoder-Based Anomaly Detection of Electricity Theft Cyberattacks in Smart Grids
IEEE Systems Journal, 2022Designing an electricity theft cyberattack detector for the advanced metering infrastructures (AMIs) is challenging due to the limited availability of electricity theft datasets (i.e., malicious datasets).
Abdulrahman Takiddin +3 more
semanticscholar +1 more source
3D MRI brain tumor segmentation using autoencoder regularization
BrainLes@MICCAI, 2018Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease.
Andriy Myronenko
semanticscholar +1 more source
High-Ratio Lossy Compression: Exploring the Autoencoder to Compress Scientific Data
IEEE Transactions on Big Data, 2023Scientific simulations on high-performance computing (HPC) systems can generate large amounts of floating-point data per run. To mitigate the data storage bottleneck and lower the data volume, it is common for floating-point compressors to be employed ...
Tong Liu +5 more
semanticscholar +1 more source
MVAE: Multimodal Variational Autoencoder for Fake News Detection
The Web Conference, 2019In recent times, fake news and misinformation have had a disruptive and adverse impact on our lives. Given the prominence of microblogging networks as a source of news for most individuals, fake news now spreads at a faster pace and has a more profound ...
Dhruv Khattar +3 more
semanticscholar +1 more source
IEEE Transactions on Dependable and Secure Computing, 2023
The importance of understanding and explaining the associated classification results in the utilization of artificial intelligence (AI) in many different practical applications (e.g., cyber security and forensics) has contributed to the trend of moving ...
Diana Laura Aguilar +4 more
semanticscholar +1 more source
The importance of understanding and explaining the associated classification results in the utilization of artificial intelligence (AI) in many different practical applications (e.g., cyber security and forensics) has contributed to the trend of moving ...
Diana Laura Aguilar +4 more
semanticscholar +1 more source