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Found 10 papers, 6 papers with code
Modeling glycemia in humans by means of Grammatical Evolution
Diabetes mellitus is a disease that affects to hundreds of millions of people worldwide.
R&R: Metric-guided Adversarial Sentence Generation
It improves the quality of adversarial examples by optimizing a critique score which combines the fluency, similarity, and misclassification metrics.
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations.
Adversarially learned anomaly detection for time series data
Based on the recent developments in adversarially learned models, we propose a new approach for anomaly detection in time series data.
Robust Invisible Video Watermarking with Attention
The goal of video watermarking is to embed a message within a video file in a way such that it minimally impacts the viewing experience but can be recovered even if the video is redistributed and modified, allowing media producers to assert ownership over their content.
Towards Reducing Biases in Combining Multiple Experts Online
In many real life situations, including job and loan applications, gatekeepers must make justified and fair real-time decisions about a person's fitness for a particular opportunity.
Modeling Tabular data using Conditional GAN
Tabular data usually contains a mix of discrete and continuous columns.
SteganoGAN: High Capacity Image Steganography with GANs
Image steganography is a procedure for hiding messages inside pictures.
Learning Vine Copula Models For Synthetic Data Generation
Moreover, we demonstrate that the model is able to generate high-quality samples in a variety of applications, making it a good candidate for synthetic data generation.
ATM: A distributed, collaborative, scalable system for automated machine learning
In this paper, we present Auto-Tuned Models, or ATM, a distributed, collaborative, scalable system for automated machine learning.
