Unreasonable iteration number and step-size setting limit the performance of adversarial attacks. To deal with this issue, we propose a variable step-size double-iteration (VSDI) white-box attack. Due ...

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

Competitive endogenous RNA (ceRNA) regulatory networks (CENA) have advanced our understanding of noncoding RNAs’ roles in complex diseases, providing a theoretical basis for disease mechanisms.

Learning to discover hidden variables from unlabeled data is an important task. Traditional generative methods model the generation process of the observed variables as well as the hidden variables.

Hi, Thanks for your great work with MAISI. The mask_generation_autoencoder has 8 input channels. What is the kind of data that is required as input? Would you have an example on how to use the mask ...

The autoencoder is an unsupervised deep neural network that learns a compressed representation from the input data and reconstructs an output that is as similar as possible to the original data.

Autoencoder for Product Matching This was an experiment for a possible PhD topic. The main idea was to use different Autoencoder for entity resolution / product matching. The core idea was to pretrain ...